Published in BecomingHuman.ai
Tldr: Corporate AI failures can be ascribed to poor Intuition, Process, Systems, People
The promise of AI is real. We are at the crossroads of the next industrial revolution where AI is automating industrial processes and technologies that were hitherto considered state-of-the-art. AI is expected to create global commercial value of nearly USD 13 Trillion by 2030 (McKinsey Global Institute). Given the immense commercial value that AI can unlock, it is no surprise that businesses of all kinds and sizes have jumped on the AI bandwagon and are repositioning themselves as ‘AI-first’ or ‘AI-enabled.
However, the groundbreaking progress and transformation that AI has brought across industry belies the stark reality of an increasing number of failed AI projects, products and companies (e.g. IBM Watson, and many more).
How can startups and large enterprises battle these tough odds to drive innovation and digital transformation across the organization? In this blog, I will examine from first principles common themes that typically underlie failed AI projects in corporations, and questions business leaders and teams should address when embarking on AI projects.
I have classified these under four broad areas and will tackle each of these themes individually in future blog posts:
Part 1: Intuition (Why)
Commercial AI projects often fail due to a lack of organizational understanding of the utility of AI vis-a-vis the business problem(s) to be solved. More often than not, throwing a complex AI-based solution at a problem is not the right approach, where a simpler analytical or rule-based solution is sufficient to have things up and running. It is therefore paramount to decode the business problem first and ask whether an AI approach is the only and best way forward.
Unlike software engineering projects, the fundamental unit of AI is not lines of code, but code and data. In an enterprise, data typically belongs to a particular business domain, and is generated by the interaction of customers with specific business products or services.
Here, a customer-centric approach is critical to understand the context in which this data is generated so that AI models may be developed to predict or influence user behavior to meet well-defined business objectives with clear success criteria. Wherever possible, the data scientists should themselves use and experiment with their company’s products/services by donning a ‘customer’s hat’ to decode the customer mindset. It’s hard to understand the nuances of training data if you don’t intimately understand the customer ‘persona’ to begin with.
Data reflects more than just mere numbers. Making sense of data requires a holistic cross-functional understanding from a business, product, customer as well as technical perspective. Typically, these functional roles are played by different teams within a company, necessitating a strong collaborative effort to demystify the business problem, question the existing solutions and come up with new hypotheses, test and prove or disprove these hypotheses quickly via iterative experiments to hone in on a feasible solution and strategy.
Here, the importance of domain knowledge or subject matter expertise cannot be stressed enough. It takes years to gain deep domain expertise which enables practitioners to develop better intuition for the business problem and the underlying data to propose feasible solutions or strategies.
As data scientists typically lack expertise in business domains, it is imperative they complement their algorithmic data science skills with expert knowledge from those who work closely with the customer and understand the business problem intimately.
Tldr (Part 1/4):
Ask why is AI needed for your business problem? Is it the only way to solve the problem? And if yes, build and test hypotheses by leveraging the collective organizational knowledge and intuition across cross-functional teams that specialize in data science, business, product, operations.
TLDR (or TL;DR) is a common internet acronym for “Too Long; Didn’t Read.” It likely originated on the comedy forum Something Awful around 2002 (source) and then became more popular in online forums like Reddit. It is often used in social media where the author or commenters summarise lengthy posts and provide a TLDR summary of one or two lines as a courtesy to other readers. TLDRs help readers get the gist of the information and enable quick informed decisions on whether to invest the time in reading the full post.
With Natural language processing (NLP) and automatic text summarization systems, TLDR generation can be automated. Automatic text summarization is a challenging problem of generating a shorter summary of a long document while preserving its essence. It has wide practical applications in multiple domains such as legal contract analysis, search (summarising use information in websites, entity-centric summarization from Wikipedia articles), question answering systems, media (generating news headlines, summarising articles in newsletters), marketing (generating copy, slogans) among others. Automatically generated text summaries help reduce reading time, are non-biased compared to human authored summaries, and could also be beneficial for a lot of personal day to day applications like email summarization, TLDR generation for posting on social media sites like Twitter, and more.
👉 Here is the full article
Published in KDNuggets
Coughing and sneezing were believed to be symptoms of the bubonic plague pandemic that ravaged Rome in the late sixth century. The origins of the benevolent phrase, “God bless you,” after a person coughs or sneezes is often attributed to Pope Gregory I, who hoped that this prayer would offer protection from certain death. The flu-like symptoms associated with the plague co-occur during the current Covid-19 pandemic as well, to the extent where “normal” coughs draw immediate alarm and concern. However, in the present technologically advanced times, we need not resort only to prayers. We can now build sophisticated AI models that learn complex acoustic features to distinguish between cough sounds from Covid-19 positive and otherwise healthy patients.
Since the start of the Covid-19 pandemic, multiple AI research teams have been working towards leveraging AI to improve screening, contact tracing, and diagnosis. Most of the preliminary work involved CT or X-ray scans [1,2,3,4] to diagnose Covid-19 faster and, in some cases, with better accuracy than the RT-PCR test. Recently, AI researchers have started testing cough sounds for preliminary diagnosis or a prescreening technique for Covid-19 detection in asymptomatic individuals. This is beneficial because, while someone may not have noticeable symptoms, the virus may still cause subtle changes in their body that may be detected by specific algorithms combining audio signal processing and machine learning. Cough-based audio diagnosis is non-invasive, cost-effective, scalable, and, if approved, could be a potential game-changer in our fight against Covid-19. This technology might also prove to have better efficacy than the standard strategy of prescreening for Covid-19 on the basis of temperature, especially for asymptomatic patients.
The intuition behind using cough sounds
Cough, along with fever and fatigue, is one of the key symptoms of Covid-19 . Studies have shown that cough from different respiratory ailments has unique characteristics due to the different nature and location of the underlying irritants . Though a human ear cannot differentiate these features, AI models can be trained to learn these features and discriminate between a cough from a Covid-19 positive and negative patient.
One of the significant challenges is the availability of the right quantity and quality of data to build an AI model that can make robust predictions about the underlying medical ailment based on cough sounds. Cough is, unfortunately, a common symptom of many respiratory and non-respiratory diseases (see Figure 2). Hence, an AI model must also learn to distinguish coughs related to Covid-19 from coughs caused by other respiratory ailments. The prediction of such AI models could be considered as such or be further substantiated by other clinical tests, for instance, an RT-PCR screening test.
Since spring 2020, AI researchers have collected cough sound data from the general public via mobile apps and websites and developed AI solutions for cough-based prescreening tools. Some of these works include - AI4Covid-19  from the University of Oklahoma, Covid-19 sounds  from the University of Cambridge, Coswara  from IISC Bangalore, Cough against Covid-19  from Wadhwani AI, Covid-19 Voice detector  from CMU, COUGHVID from EPFL , Opensigma from MIT , Saama AI research  and UK startup Novoic amongst others.
While the cough data in the AI4Covid-19, Cough against Covid-19, and Saama AI research projects are collected in a controlled setting or collected from hospitals under clinical supervision, Coswara, Covid-19 sounds, and COUGHVID, MIT’s project, use crowdsourced and uncontrolled data collected through their websites or app. The website/app records forced coughs (Coswara also collects more audio - breathings sounds, vowel pronunciations, counting numbers from one to twenty) and gather useful metadata like age, gender, ethnicity, and health status information, like details of a recent Covid-19 test, current symptoms, and health status, like the occurrence of diabetes, asthma, heart disease, amongst others.
The AI4Covid-19, Covid-19 sounds, and Saama AI research projects also train models to differentiate Covid-19 cough sounds from non-Covid-19 infection coughs like pertussis, asthma, and bronchitis. MIT researchers used features from their previous work to detect Alzheimer’s from cough sounds  and fine-tuned their AI model to detect Covid-19 from a healthy person’s cough. The connection between Covid-19 and the brain with recently reported symptoms of neurological impairments in Covid-19 patients led authors to test the same biomarkers - vocal cord strength, sentiment, lung performance, and muscular degradation for detecting Covid-19 coughs.
“Our research uncovers a striking similarity between Alzheimer’s and Covid-19 discrimination. The exact same biomarkers can be used as a discrimination tool for both, suggesting that perhaps, in addition to temperature, pressure, or pulse, there are some higher-level biomarkers that can sufficiently diagnose conditions across specialties once thought mostly disconnected.” 
Once an AI model is trained, it can be incorporated into a user-friendly app where users can log in and submit their cough sounds via their phones to get instant results. The model prediction can be used to ascertain whether a user might be infected and follow-up to confirm with a formal test like RT-PCR. Figure 5 shows an overview of the architecture developed by the AI4covid-19 team. It includes a cough detection model to check the quality of the cough sound and prompts the user to re-record in case of noisy recording or non-cough sound. The detected cough is then sent to Covid-19 diagnosis model(s) to discriminate between a cough from a Covid-19 positive and negative patient.
The preliminary results of most of the teams look promising and confirm the hypothesis that cough sounds contain unique information and latent features to aid diagnosis and prescreening for Covid-19. The MIT lab has collected around 70,000 audio samples of different coughs with 2,500 coughs from confirmed Covid-19 positive patients. The trained model correctly identified 98.5% of people with Covid-19 and correctly ruled out Covid-19 in 94.2% of people without the disease. For asymptomatic patients, the model correctly identified 100% of people with Covid-19, and correctly ruled out Covid-19 in 83.2% of people without the disease. Cambridge’s Covid-19 sounds project reported an 80% success rate in July 2020.
In spite of the similar acoustic modeling pipeline and deep learning approaches, it is difficult to compare these preliminary results across these projects as each AI model is trained using distinct datasets (owing to the scarcity of publicly available datasets to different benchmark works). Since cough also covaries with age and gender, it is important to collect diverse data to make any AI solution generalize across patient populations around the world and accepted as a standard non-invasive prescreening tool for Covid-19. The data collection for most of the projects is still ongoing, and readers are suggested to check out these websites, donate coughs, and help save lives: Covid-19 sounds, Coswara, Cough against Covid-19, Covid-19 Voice detector, COUGHVID, Opensigma, Novoic, and AI4COVID-19.
 L. Wang, A. Wong ‘‘Covid-19-Net: a tailored deep convolutional neural network design for detection of Covid-19 cases from chest radiography images,’’ (2020) arXiv preprint arXiv:2003.09871vol. 1
 Zhang I, Xie Y, Li Y, Shen C, Xia Y. ‘‘Covid-19 screening on chest X-ray images using deep learning based anomaly detection,’’. 2020. arXiv preprint arXiv: 2003.12338.
 Li L, Qin L, Xu Z, Yin Y, Wang X, Kong B, Bai J, Lu Y, Fang Z, Song Q, et al. ‘‘Artificial intelligence distinguishes Covid-19 from community acquired pneumonia on chest ct. ’’ Radiology; 2020. 200905
 Zhao W, Zhong Z, Xie X, Yu Q, Liu J. ‘‘Relation between chest ct findings and clinical conditions of coronavirus disease (Covid-19) pneumonia: a multicenter study. ’ American Journal of Roentgenology 2020:1–6.
 WHO. 2020b. Q&A on coronaviruses (COVID19). https://www.who.int/emergencies/diseases/novelcoronavirus-2019/question-and-answers-hub/q-a-detail/qa-coronaviruses. Accessed: 2020-11-17.
 Imran, Ali, et al. "AI4COVID-19: AI enabled preliminary diagnosis for COVID-19 from cough samples via an app." (2020) arXiv preprint arXiv:2004.01275
 Brown, Chloë, et al. "Exploring Automatic Diagnosis of COVID-19 from Crowdsourced Respiratory Sound Data." (2020) arXiv preprint arXiv:2006.05919
 Sharma, Neeraj, et al. "Coswara--A Database of Breathing, Cough, and Voice Sounds for COVID-19 Diagnosis." (2020) arXiv preprint arXiv:2005.10548
 Bagad, Piyush, et al. "Cough Against COVID: Evidence of COVID-19 Signature in Cough Sounds." arXiv preprint arXiv:2009.08790 (2020).
 Deshmukh, Soham, Mahmoud Al Ismail, and Rita Singh. "Interpreting glottal flow dynamics for detecting COVID-19 from voice." arXiv preprint arXiv:2010.16318 (2020).
 Orlandic, Lara, Tomas Teijeiro, and David Atienza. "The COUGHVID crowdsourcing dataset: A corpus for the study of large-scale cough analysis algorithms." arXiv preprint arXiv:2009.11644 (2020).
 Laguarta, Jordi, Ferran Hueto, and Brian Subirana. "COVID-19 Artificial Intelligence Diagnosis using only Cough Recordings." IEEE Open Journal of Engineering in Medicine and Biology (2020).
 Pal, Ankit, and Malaikannan Sankarasubbu. "Pay Attention to the cough: Early Diagnosis of COVID-19 using Interpretable Symptoms Embeddings with Cough Sound Signal Processing." arXiv preprint arXiv:2010.02417 (2020).
 J. Laguarta, F. Hueto, P. Rajasekaran, S. Sarma, and B. Subirana, “Longitudinal speech biomarkers for automated alzheimer’s detection,” Cognitive Neuroscience, Preprint, pp. 1–10, 2020. https://www.researchsquare.com/article/rs-56078/latest.pdf
Published in BusinessWorld
The promise of AI is real. Research from Accenture posits that AI could add $ 957 billion to the Indian economy and raise India’s income by 15 percent in 2035. Globally, the economic value that AI is expected to create close to $ 13 trillion by 2030. However, the stark reality is that India has close to 100,000 vacant data scientist jobs as of today, with the demand for AI-centric roles set to increase exponentially. How can India possibly unlock this massive economic potential of AI, without an established talent pipeline?
The lack of an established AI talent pipeline for a rapidly modernizing economy like India is alarming. While India has a working age population of close to 589 million, only 49 percent are said to possess digital skills, with the proportion of those able to understand and build AI products is far lower (World Economic Forum). Although the supply of engineering talent is steady, the nature of the rapidly changing jobs landscape means that core engineering jobs are transforming into digital roles that require strong software engineering and programming skills.
Not only Indian universities have failed to keep pace with adapting the course curricula to the skills requirements of the modern data-driven industries but the consequences of not training candidates in fundamental data skills and leadership skills to build collaborative AI projects can be even more damaging to the economy in the long run. Academia suffers from an acute shortage of expert faculty to train students in state-of-the-art AI theory and practical knowledge at scale. This burden of nurturing and creating AI talent does not rest solely with educational institutions. Industry needs to step up and actively contribute by sharing business data, a critical ingredient for building data-hungry supervised AI systems, and foster a vibrant and collaborative ecosystem by partnering with both academia and startups to raise awareness of the kind of challenging business problems that only AI can solve effectively.
To bridge the gap between industry requirements of AI talent and lack of industry- oriented AI education at universities, a number of edtech startups have stepped up. The majority of online edtech platforms focus on programming and coding skills, a key foundational skill to building AI systems. However, the pedagogical methods practised by most suffer from lack of imagination and creativity and do not innovate beyond offering the age-old offline classroom content via online platforms - the adage ‘old wine in a new bottle’ comes to mind.
AI is a multidisciplinary field that requires strong creative, scientific and problem solving abilities to come up with novel solutions to pressing business problems. The ability to innovate beyond open-source models and solutions is fundamental to building tailored customer-centric AI solutions that incorporate the unique business and cultural context of India.
If India is not able to keep pace with AI global superpowers like the USA and China, then not only is she at risk of lagging behind in the battle for tech supremacy but also faces the dire prospect of losing its emerging tech talent to countries that offer better opportunities to work at the cutting edge of AI. India is set to become the world’s youngest country with 64 percent of its population in the working age group, while western countries, China and Japan have an aging demographic. India must therefore implement policy changes, state-wide reskilling initiatives in cooperation with industry, academia and startups to reskill the nation’s youth in the latest digital and AI-first skills to steer India into the next decade as a leading digital economy.
Fact-checking Covid-19 Fake News
Electronic means of communication have helped to eliminate time and distance barriers to sharing and broadcasting information. However, despite all its advantages, faster means of communication have also resulted in the extensive spread of misinformation. The world is currently going through the deadly COVID-19 pandemic and fake news regarding the disease, its cures, its prevention, and causes have been broadcast widely to millions of people. The spread of fake news and misinformation during such precarious times can have grave consequences leading to widespread panic and amplification of the threat of the pandemic itself.
As per a recent BBC report from August 2020, at least 800 people may have died around the world because of coronavirus-related misinformation in the first three months of this year. It is therefore of paramount importance to limit the spread of fake news and ensure that accurate knowledge is disseminated to the public.
In this blog, we explore the problem of fake news detection related to COVID-19 and describe our approach to tackle it using Natural Language Processing. This is based on our recent paper — ‘Two Stage Transformer Model for COVID-19 Fake News Detection and Fact Checking’, accepted at the NLP for Internet Freedom Workshop, co-located with COLING2020.
Our NLP solution:
We built a topical fake news detection system capable of verifying claims as well as providing explanations, all in real-time. Developing a solution for such a task involves generating a database of factual explanations, which constitutes our knowledge base, that serves as ground truth for any given claim. We computed the entailment between any given claim and explanation to verify whether the claim is true or not. Querying for claim-explanation pairs for each explanation in our knowledge base is computationally expensive and slow, so we propose generating a set of candidate explanations that are contextually similar to the claim. We achieved this by using a model trained with relevant and irrelevant claim-explanation pairs and using a similarity metric between the two to match them.
Previous research on fake news detection
Previous work on fake news detection has primarily focused on evaluating the relationship measured via a textual entailment task between a header and the body of the article. Researchers have explored the use of simple classifier models with TF-IDF features and cosine similarity metric to classify fake news. Several baselines with such methods exist on standard datasets like FNC-1 and FEVER.
Transformer based pre-trained models achieved state of the art results in several NLP subtasks, their ease of fine-tuning makes them adaptable to newer tasks. In further related work, the authors proposed a model based on the BERT architecture to detect fake news by analyzing the contextual relationship between the headline and the body text of news. They further enhanced their model performance by pre-training with domain-specific news and articles.
The use of social media has also been extensively studied for stopping misinformation for Covid-19. In a related work to this, authors developed an Infodemic Risk Index (IRI) after analyzing Twitter posts across various languages and calculated the rate at which a particular user from a locality comes across unreliable posts from different classes of users like verified humans, unverified humans, verified bots and unverified bots.
But none of these mentioned works tackles the problem of misinformation by reasoning out the given fake claim with an explanation.
The use of an existing misinformation dataset would not serve as a reliable knowledge base for training and evaluating the models due to the recent and uncommon nature i.e., the vocabulary used to describe the disease and the terms associated with the COVID-19 pandemic.
It was therefore important to generate real and timely datasets to ensure accurate and consistent evaluation of the methods.
To overcome this drawback, we manually curated a dataset specific to COVID-19. Our proposed dataset consists of 5500 claim and explanation pairs. There are multiple sources on the web that are regularly identifying and debunking fake news on COVID-19. We collected data from “Poynter”, a fact checking website which collects fake news and debunks or fact-checks them with supporting articles from more than 70 countries.
For each fact check, we collected only the ”claim” and the corresponding “explanation” from this database which were rated as ’False’ or ’Misleading’. In this way, we collected about 5500 false-claim and explanation pairs. We further manually rephrased a few of these false claims to generate a true claim, as the ones that aligned with the explanation, so as to create an equal proportion of true-claim and explanation pairs.
The architecture consists of a two stage model, we will refer to the first model as “Model A” and the second model as “Model B”. The objective of Model A is to fetch the candidate “true facts” or explanations for a given claim, which are then evaluated for entailment using Model B.
Model A is trained on all claim-explanation pairs, as we have a lot more of them, and the task of the Model A is to pick out candidate claims for a given explanation. Model A is trained on a Next Sentence Prediction (NSP) task.
Through our experiments, we find that, on this trained model, if we generate embeddings for a single sentence (either claim or explanation individually) and compare matching [claim, explanation] embeddings using the cosine similarity metric, there is a distinction in the distribution of similarity scores between related and unrelated [claim, explanation] pairs.
Therefore, for faster near real-time performance, we cache the embeddings for all our explanations (knowledge base) beforehand and compute the cosine similarity between the claim and the cached embeddings of the explanations. We fetch the top explanations for any given claim exceeding a certain threshold of sentence similarity as there could be several explanations relevant for a given claim.
The second part of the pipeline is to identify the veracity of a given claim. Model A fetches the candidate explanations while Model B is used to verify whether the given claim aligns with our set of candidate explanations or not. To train Model B, we use a smaller subset of “false claim” and “explanation” pairs from our original dataset, and cross-validate each sample with “true claim” or in other words, claims that align with the factual explanation.
However, this small annotated data is not sufficient to train the model effectively. Therefore, the parameters of the Model A, which was trained on a much larger dataset were used as initial parameters for Model B, and fine-tuned further using our cross-validated dataset. Model B is also trained for the sequence classification task. Essentially Model B computes the entailment between its input claim, explanations pairs.
We trained and evaluated both Model A and Model B using several approaches based on classical NLP methods as well as more sophisticated pre-trained Transformer models. The flow of the Model A + Model B pipeline is shown in the above figure.
Transformer based Models:
We trained and evaluated three Transformer based pre-trained models for both Model A and Model B using the training strategy described before. As our focus was to ensure that the proposed pipeline can be deployed effectively in a near real-time scenario, we restricted our experiments to models that could efficiently be deployed using inexpensive compute. We chose the following three models — BERT(base), ALBERT, and MobileBERT.
Model A was trained on 5000 claim-explanation pairs on the sequence classification task to optimize the softmax cross entropy loss. This trained model was then validated on a test set comprising 1000 unseen claim-explanation pairs. The training data structure here looks like this.
[claim, relevant explanation, 1], [claim, irrelevant explanation, 0]
Model B was trained on a smaller subset of 800 cross-validated [claim, explanation, label] data, on the same sequence classification task, where the label was assigned based on whether the claim aligned with the explanation — 1 or not — 0. This was validated on 200 unseen data-points. The loss function used was softmax cross-entropy. The training data structure here looks like:
[true claim, relevant explanation, 1] [false claim, relevant explanation, 0]
For baselining we implement classical NLP approaches in our use-case and compare those results with transformer based models. We implement GLoVeand TF-IDF architectures for the classical ones.
For evaluating the performance of the overall pipeline model, we first evaluate the performance of Model A in its ability to retrieve relevant explanations. For this we use Mean Reciprocal Rank(MRR) and Mean Recall@10, that is, the proportion of claims for which the relevant explanation was present in the top 10 most contextual explanation by cosine similarity and their mean inverse rank.
Once, Model A has retrieved relevant explanations, we evaluate the performance of Model B on computing the veracity of the claim. Here, we only used explanations that exceed an empirically defined threshold in cosine similarity between the query claim and the explanation. Through our experiments, we found that a threshold of the mean standard deviation of cosine similarity over the validation data worked well for picking relevant explanations. For evaluating the accuracy, we take a mean of the output probabilities for each claim, explanationᵢ.
This is expected due to the lower parameter size of the TF-IDF and GloVe models. Among the Transformer based models, MobileBERT had the least latency per claim as expected while ALBERT consumed the least memory. The best performing BERT+ALBERT model utilized a memory of 1398MB and fetched relevant explanations of each claim in 2.471 seconds. The model latencies and memory usage were evaluated on an Intel Xeon — 2.3GHz Single-core — 2 thread CPU.
We however do acknowledge that our models could still make errors of two kinds:
Model A might not fetch a relevant explanation which automatically means that the prediction provided by Model B is irrelevant,
Model A might have fetched the correct explanation(s) but Model B classifies it incorrectly. We show some of the errors our models made in this table.
In this work, we have demonstrated the use and effectiveness of pre-trained Transformer based language models in retrieving and classifying fake news in a highly specialized domain of COVID-19. Our proposed two stage model performs significantly better than other baseline NLP approaches. Our knowledge base, which we prepare through collecting factual data from reliable sources from the web can be dynamic and change to a large extent, without having to retrain our models again for as long as the distribution is consistent. All of our proposed models can run in near real-time with moderately inexpensive compute. Our work is based on the assumption that our knowledge base is accurate and timely.
This assumption might not always be true in a scenario such as COVID-19 where “facts” are changing as we learn more about the virus and its effects. Therefore a more systematic approach is needed for retrieving and classifying claims using this dynamic knowledge base.
Our future work consists of weighting our knowledge base on the basis of the duration of the claims and benchmarking each claim against novel sources of ground truth.
Our model performance can be further boosted by better pre-training, through domain specific knowledge. In one of the more recent work, the authors propose a novel semantic textual similarity dataset specific to COVID-19. Pre-training our models using such specific datasets could help in a better understanding of the domain and ultimately better performance. Fake news and misinformation is an increasingly important and difficult problem to solve, especially in an unforeseen situation like the COVID-19 pandemic.
Leveraging state of the art machine learning and deep learning algorithms along with preparation and curation of novel datasets can help address the challenge of fake news related to COVID-19 and other public health crises.
In recent years, the amount of data powering different industries, and their systems has been increasing exponentially. Majority of business information is stored in the form of relational databases that store, process, and retrieve data. Databases power information systems across multiple industries, for instance, consumer tech (e.g. orders, cancellations, refunds), supply chain (e.g. raw materials, stocks, vendors), healthcare (e.g. medical records), finance (e.g. financial business metrics), customer support, search engines, and much more.
It is imperative for modern data-driven companies to track the real-time state of its business in order to quickly understand and diagnose any emerging issues, trends, or anomalies in the data and take immediate corrective actions. This work is usually performed manually by business analysts who compose complex queries in declarative query languages like SQL to derive business insights stored in multiple tables. These results are typically processed in the form of charts or graphs to enable leadership teams to quickly visualize the results and facilitate data-driven decision making.
Although the most common SQL queries that address fundamental business metrics are predefined and incorporated in commercial products like PowerBi that power insights into business metrics, any new or follow-up business queries still need to be manually coded by the analysts. Such static interactions between database queries and consumption of the corresponding results require time-consuming manual intervention and result in slow feedback cycles. It is vastly more efficient to have non-technical business leaders directly interact with the analytics tables via natural language queries that abstract away the underlying SQL code.
Defining the SQL query requires a strong understanding of database schema, SQL syntax and can quickly get overwhelming for beginners and non-technical stakeholders. Efforts to bridge this communication gap have led to the development of a new type of processing called Natural Language Interface to Database (NLIDB). This natural search capability has become more popular over recent years as companies such as Microsoft , Salesforce , and others are developing similar technologies for Natural language (NL) to SQL (NL2SQL). The converted SQL could also enable virtual assistants like Alexa, Google Home, and others to improve their responses when the answer can be found in different databases or tables. This blog will review the challenges, evaluation methods, datasets, different approaches, and some state-of-the-art deep learning approaches for NL2SQL.
2. Technical challenges
2.1 Understanding NL query and aligning utterance with schemaThe system must understand both the user’s question and the table schemas (columns, table names, and values) to map the query to SQL correctly. A key challenge here is understanding the structured schema of DB tables (e.g., the name, data type, and stored values of columns) and the alignment between the input query and the schema. For instance, for the question, Which country has the largest GDP?, the model needs to map GDP to the Gross Domestic Product Column. Sometimes the question might also require understanding the semantics of a column rather than just column names.
For the table and question shown in Figure 3, the Venue column used to answer the example question refers to host cities. Hence, the model needs to align “city” in the query with the venue column in the table.
2.2 Generalization to cross-domains
Collecting large training data for different domains is expensive and non-scalable. Hence, it is important to train systems to generalize to different domains and databases. This generalization would involve identifying new entities, mapping unseen phrases and entities correctly in the SQL query, and handling novel database and query structures (larger tables, the composition of SQL components, etc.).
2.3 Order matters problem
One of the standard ways to solve the NL2SQL tasks is to use seq2seq (since both NL query and SQL are sequences) models and their variants. One of the issues with this approach is that different SQL queries may be equivalent to each other due to commutative and associative properties.
There are several datasets for NL2SQL tasks. These contain annotated NL questions, SQL pairs corresponding to one or more tables. These datasets differ in terms of domains (single vs. cross-domain), size (number of queries — which is essential for proper model evaluation), and query complexity (single table vs. multi-table).
The early datasets like ATIS, GeoQuery focus on single domains and are also limited in terms of the number of queries. Some of the latest datasets like WikiSQL, Spider are cross-domain, and context-independent with a larger size. One significant difference between WikiSQL and Spider is query complexity. Queries in WikiSQL are simpler (which only covers SELECT and WHERE clauses). Also, each database in WikiSQL is only a simple table without any foreign key. Spider contains a modest number of queries and includes complex questions that involve joins of tables and nested queries. The SParC and CoSQL are the extensions of the Spider dataset that are created for contextual cross-domain semantic parsing and conversational dialog text-to-SQL system.
4. Evaluation methods
The most common methods to evaluate NL2SQL systems are execution accuracy and logical form accuracy.
Execution accuracy compares the result after execution of the predicted SQL query with the result of the ground truth query. One downside of this method is that it is possible to have an unrelated SQL query that does not correspond to the question but still gives the right answer (for example, NULL result).
Logical form accuracy compares the exact string match of predicted SQL query with the ground truth query. This metric has the limitation of incorrectly penalizing predictions that yield correct results upon execution but do not have an exact string match with a ground truth SQL query. One approach to solving the ordering issue is to canonicalize SQL queries before comparison . SQL canonicalization is a method to make evaluations consistent by ordering columns in SELECT, tables in FROM, and WHERE constraints and standardizing table aliases, capitalization, and space between symbols.
Authors of Spider  use component matching, which measures the average exact match between the prediction and ground truth on different SQL components like SELECT, WHERE, GROUP BY, etc. The prediction and ground truth is parsed and decomposed into subcomponents and then their exact match is calculated component-wise.
For example, to evaluate the SELECT component:
SELECT avg(col1), max(col2), min(col1) is decomposed to set (avg, min, col1), (max, col2)
And then this set is compared with the ground truth sets.
Even though this takes care of the ordering issue, it still does not account for when the prediction uses a different logic (compared to ground truth SQL) to arrive at the same result. Hence for a thorough evaluation, execution accuracy should also be used.
The authors in  also categorize query by hardness based on the number of SQL components, selections, and conditions. This categorization can be very helpful for getting more insights about model performance with respect to query complexity.
5. Different approaches for NL2SQL
5.1 Rule-based approachesMost existing approaches focus on a rule-based parser for natural language combined with ambiguity detection. Some rule-based systems use trigger words to identify patterns in the user’s question. For example, “by” is a common word used in aggregation queries like “List the movies directed by <director>”. Here, the trigger word’s left side might have the keywords required for the SELECT clause, and the right side would have the necessary keywords for the GROUP BY clause.
Despite its simplicity, this approach (if rules are well-formed) has been shown to handle a surprisingly broad type of queries. Modern conversational agents such as Siri and Cortana follow a similar principle, although the rules are not deterministic and based on training (logistic regression classifier of intent).
5.2 Grammar-based systemsIn Grammar-based systems, the user’s question is parsed, and the resulting parsed tree is directly mapped to expression in SQL. A grammar is created which can describe the possible syntactic structures of the user’s questions.
The over-simplistic grammar shown in the figure considers the user’s question(S) to be composed of Noun Phrase and a Verb Phrase; Noun phrases consist of a Determiner followed by Noun, Determiner consists of the word “What” or “Which” etc.
This grammar can then be used to parse a question like “Which rock contains magnesium?” into a parse tree and then map the resulting parse tree to SQL. This mapping back to SQL would be carried out by rules and based entirely on the parse tree’s syntactic information.
5.3 Deep Learning based-approachesRule-based approaches are limited in terms of coverage, scalability, and naturalness. They are also not robust to natural language diversity and are very difficult to scale across domains. The advent of large scale supervised datasets like WikiSQL, Spider, etc., and advances in Natural language processing, pretraining , etc. has enabled Deep learning models to achieve the state of the art results in NL2SQL tasks.
Almost all the deep learning models generate the SQL query from natural language input with an encoder-decoder  model. The encoder could be RNN  / LSTM  or the recent transformer  networks. Most of the models differ in how they encode the schema (table names, column names, cell values, etc.) and how they produce the SQL output.
Some models make schema as part of their output vocabulary. In other words, they put all the table names, column names, etc., into their output vocabulary, and while decoding the SQL output, they select these words from the vocabulary. NSP, DBPAL are some of the methods which use this approach. One major limitation of this approach is we cannot adapt them to cross-domains as they do not encode new schemas in their input.
In contrast, other methods like SEQ2SQL use the schema as input to the model and while decoding, use the table or column names mentioned in the input using pointer networks . For instance, in SEQ2SQL, the authors use column names, question tokens, and SQL tokens like SELECT, WHERE, COUNT, MIN, MAX, etc. as input. Their pointer network produces the SQL query by selecting exclusively from this augmented input sequence. The authors also claim that apart from limiting the output space, this augmented pointer network produces higher quality WHERE clauses.
Based on the generation of SQL queries from natural language input, there exist three types of models: sequence to sequence, sequence to tree, and slot-filling. The sequence to sequence models generates the SQL as a sequence of words. The sequence to tree models generates a syntax tree of the predicted SQL query. The slot-filling methods treat the SQL query as a set of slots and then decode the whole question using relevant decoders for each slot. An advantage of grammar-based decoders is that they can check for grammatical errors at every step, producing complex queries with joins, nested queries, etc. without any syntax errors.
5.4 Modern Deep Learning approaches
Modern Deep Learning approaches use more techniques to learn joint representations over NL questions and structured information present in tables. They use various attention-based architectures for question/schema encoding and AST based structure architecture (sequence to tree) for query decoding. IRNet , RAT-SQL (current SOTA approach in spider)  use BERT (for NL representation) along with in-house strategies to encode structured information in tables. In contrast, TaBERT uses a general-purpose pretraining approach to learn representations of natural language sentences and tabular data. These techniques include schema linking, better schema encoding, using DB content (Cell values instead of just column and table names), contextualizing questions and schema representations.
5.4.1 Schema Linking
This involves aligning the entity references in the question to the right schema columns or tables. Textual matches are the best evidence for question-schema alignment, and it might be directly beneficial to the encoder. Linking is generally done with string matching in IRNet and RAT-SQL. N-grams (up to lengths of 5 or 6) in the question are used to match (both exact matches and partial matches are considered) column or table names in the schema. After linking, IRNet tags each entity mentioned in the question with the type of the corresponding entity (table name/column name, etc.) while encoding. The column names are also assigned types EXACT MATCH and PARTIAL MATCH based on n-gram overlap with question words. RAT-SQL, on the other hand, constructs a graph with question words and the column/table names as the nodes and edges being QUESTION-COLUMN-M, QUESTION-TABLE-M, etc., where M is either one of EXACTMATCH, PARTIALMATCH, or NOMATCH.
5.4.2 Value-based linking
The natural language question can also have value mentions (like ‘4’ in ‘For cars with 4 cylinders, which model has the largest horsepower’), which would be present as a cell value in some table. IRNet looks up the value mention from the question in a knowledge base and searches the results returned over the column names for partial or exact matches. The column names are assigned types VALUE EXACT MATCH and VALUE PARTIAL MATCH based on the match. RAT-SQL, on the other hand, adds an edge COLUMN-VALUE between question word and a column name if the question word occurs as a value in the column.
TaBERT uses the DB content directly instead of linking and using the column name. The authors reason that contents provide more detail about a column’s semantics than just the column’s name, which might be ambiguous. They select a content snapshot consisting of only a few rows that are most relevant based on string matching (n-gram overlap) to the NL question.
5.4.3 Schema Encoding
This involves encoding the relational structure in databases. It is much more challenging in databases with multi-table relations (where encoding primary, foreign keys, etc. are essential). IRNet encodes both the columns and tables to get column and row representations. The columns are represented by column name and their type, which is defined from schema linking. The final representations are created by adding the column name embeddings, context embeddings (based on n-grams matched in the question), and type embeddings.
RAT-SQL represents the schema as a directed graph with columns and tables as nodes. The edges are defined by database relations detailed in the above diagram.
5.4.4 Contextualizing question and schema representations
This helps in learning effective joint representations. RAT-SQL augments their schema graph by adding edges between question words and schema entities defined after schema linking. They introduce a relation-aware self-attention layer to use the relational structure in the input and also learn “soft” relations between the sequence elements. They do this by providing a way to communicate known relations (like primary key, foreign keys, etc. defined in edge labels) by adding their representations to the attention.
6. Conclusions and Future trends
In this blog, we reviewed the state-of-the-art in NL2SQL — the problem statement, challenges, evaluation of such systems, and the modern machine learning techniques for solving the task. Recent work also focuses on improving the user experience while using such systems. Photon  is a flexible system that supports both NL questions and SQL as input. It also has a confusion detection module that detects unanswerable questions and helps users paraphrase a question to get the right answers. Authors in  also show that incorporating human feedback can further improve the accuracy and user experience of these systems.
Although modern NL2SQL techniques achieve good accuracy on benchmark test sets, they are still far from demonstrating robust performance in production settings. In the context of business decision making, it is critical to achieving reliable performance to foster and build users’ trust in such systems. NL2SQL methods have the potential to significantly enhance the efficiency of human analysts so they can focus more time on contextual interpretation and validation of results. The output of modern end-to-end deep learning systems suffer from a lack of interpretability, and while there is significant research on how AI systems work under the hood, incorporating humans-in-the-loop to provide feedback and improve the predictive power will accelerate the adoption and use of NL2SQL systems across the modern data-driven organizations.
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