Papers by Sarthak Sarthak
An Empirical Comparison of Instance Attribution Methods for NLP (2021.naacl-main)
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| Challenge: | Influence functions provide machinery for identifying training instances that may have led to a specific prediction, but are computationally expensive and prohibitive in many cases. |
| Approach: | They evaluate the degree to which different potential instance attribution agrees with respect to the importance of training samples. |
| Outcome: | The proposed methods exhibit desirable characteristics similar to more complex methods, but are computationally expensive. |
Learning to Faithfully Rationalize by Construction (2020.acl-main)
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| Challenge: | Neural models dominate NLP but it remains difficult to know why they make specific predictions for sequential text inputs. |
| Approach: | They propose a model to produce faithful rationales for neural text classification by defining independent snippet extraction and prediction modules. |
| Outcome: | The proposed model produces faithful explanations even when the model is complex and complex. |
Identification of Fine-Grained Location Mentions in Crisis Tweets (2022.lrec-1)
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| Challenge: | Recent studies have focused on identifying informative tweets by individuals affected by a crisis, without considering their specific types. |
| Approach: | They assemble two tweet crisis datasets and manually annotate them with specific location types to facilitate progress on the fine-grained location identification task. |
| Outcome: | The proposed model performs well in both in-domain and cross-domain settings. |
An Evaluation of Progressive Neural Networksfor Transfer Learning in Natural Language Processing (2020.lrec-1)
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Abdul Moeed, Gerhard Hagerer, Sumit Dugar, Sarthak Gupta, Mainak Ghosh, Hannah Danner, Oliver Mitevski, Andreas Nawroth, Georg Groh
| Challenge: | Fine-tuning suffers from catastrophic forgetting, a problem exacerbated in natural language processing (NLP). |
| Approach: | They propose to use progressive neural networks to re-use previously learned knowledge when learning new tasks. |
| Outcome: | The proposed approach improves on common NLP tasks across a range of architectures, datasets, and tasks. |
Does BERT Pretrained on Clinical Notes Reveal Sensitive Data? (2021.naacl-main)
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| Challenge: | Pretraining large (masked) language models over EHR data has yielded consistent performance gains across tasks. |
| Approach: | They propose to use large Transformers to release pretraining models over EHRs . they propose to recover patient names and conditions associated with them . |
| Outcome: | The proposed models recover patient names and conditions associated with patients . the proposed models share the model parameters for use by other researchers . |
Multi-task Learning to Enable Location Mention Identification in the Early Hours of a Crisis Event (2021.findings-emnlp)
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| Challenge: | Social media is a platform for people to share their concerns and report information as eyewitnesses of events. |
| Approach: | They propose a multi-task learning approach to leverage available annotated data for several related tasks from the crisis domain to improve performance on a main task with limited annotation. |
| Outcome: | The proposed approach improves performance on a task with limited annotated data. |
Scalable and Robust Self-Learning for Skill Routing in Large-Scale Conversational AI Systems (2022.naacl-industry)
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| Challenge: | Existing methods to enable skill routing do not scale in terms of the number of skills and skill on-boarding. |
| Approach: | They propose a model-based approach to enable natural conversation by allowing frequent policy updates . they propose an annotation-based system, rule-based model, and bandit-based learning . |
| Outcome: | The proposed method is scalable and cost-effective, the authors show . they show that it can improve the user experience without abrupt policy changes . |
Stance Detection in COVID-19 Tweets (2021.acl-long)
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| Challenge: | a global pandemic of COVID-19 has forced major changes in our daily lives . a new stance detection dataset is being used to track the stances of Twitter users . |
| Approach: | They use Twitter stance data to collect stances on topics related to the pandemic . they train models to take advantage of large amounts of unlabeled data . |
| Outcome: | The proposed model improves on existing stance detection datasets and unlabeled data. |
Empirical Evaluation of Active Learning Techniques for Neural MT (D19-61)
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| Challenge: | Several active learning (AL) algorithms for machine translation (MT) have been well-studied for phrase-based MT. |
| Approach: | They propose to use a phrase-based algorithm to compare different AL methods in a simulated AL framework to demonstrate how unsupervised pre-training and paraphrastic embedding can be used to improve existing AL methods. |
| Outcome: | The proposed method outperforms existing methods in the context of phrase-based MT and is based on a simulated phrase-driven dataset. |
CASE: Efficient Curricular Data Pre-training for Building Assistive Psychology Expert Models (2024.findings-emnlp)
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Sarthak Harne, Monjoy Choudhury, Madhav Rao, T Srikanth, Seema Mehrotra, Apoorva Vashisht, Aarushi Basu, Manjit Sodhi
| Challenge: | Existing methods to identify mental health disorders rely on limited availability of psychologists. |
| Approach: | They propose to use forum posts to analyze text data to identify mental health issues . they propose to utilize readily available curricular texts for pre-training pipelines . |
| Outcome: | The proposed pipelines achieve an f1 score of 0.91 for Depression and 0.88 for Anxiety compared to existing pipelines. |
Learning to Relate from Captions and Bounding Boxes (P19-1)
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| Challenge: | Existing methods for classifying images without supervision are limited. |
| Approach: | They propose a top-down attention mechanism to align entities in captions to objects in the image and leverage the syntactic structure of captions for alignment. |
| Outcome: | The proposed model achieves a recall@50 of 15% and recall@100 of 25% on the relationships present in the image and predicts relations that are not present in captions. |
Influence Functions for Sequence Tagging Models (2022.findings-emnlp)
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| Challenge: | Named Entity Recognition, Part-of-Speech tagging, and Semantic Role Labeling are standard tasks in NLP, but there has been little work on interpretability methods for sequence taging. |
| Approach: | They propose to extend influence functions to sequence tagging tasks by identifying noisy annotations in NER corpora. |
| Outcome: | The proposed methods are able to identify noisy annotations in NER corpora and are scalable. |
Learning Disentangled Representations of Texts with Application to Biomedical Abstracts (D18-1)
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| Challenge: | a method for learning disentangled representations of texts that encode distinct and complementary aspects is proposed . a classic problem in distributed representation learning is that it is difficult to determine what information individual dimensions encode. |
| Approach: | They propose a method for learning disentangled representations of texts that encode distinct and complementary aspects by a adversarial objective based on the (dis)similarity between triplets of documents with respect to specific aspects. |
| Outcome: | The proposed method can be used to perform aspect-specific retrieval on biomedical abstracts. |
SVeritas: Benchmark for Robust Speaker Verification under Diverse Conditions (2025.findings-emnlp)
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| Challenge: | Existing benchmarks only evaluate a subset of potential conditions, missing others entirely. |
| Approach: | a new benchmark suite evaluates speaker verification models under a variety of stressors . a san francisco-based team evaluates models under natural and background conditions . |
| Outcome: | a new benchmark suite evaluates speaker verification models under stressors under a variety of conditions . the results show that some models perform better under stress conditions than others . |
Jointly Learning to Align and Translate with Transformer Models (D19-1)
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| Challenge: | Existing word alignment models are not accurate for word alignments. |
| Approach: | They propose a method to train a Transformer model to produce accurate translations and alignments. |
| Outcome: | The proposed model outperforms GIZA++ trained models on translation and alignment tasks while maintaining translation accuracy. |
SciREX: A Challenge Dataset for Document-Level Information Extraction (2020.acl-main)
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| Challenge: | Conventional datasets and methods for information extraction focus on within-sentence relations from general Newswire text. |
| Approach: | They propose a document-level IE dataset that integrates automatic and human annotations to annotate entities and document- level N-ary relation identification from scientific articles. |
| Outcome: | The proposed dataset extends state-of-the-art IE models to document-level IE. |
Combining Feature and Instance Attribution to Detect Artifacts (2022.findings-acl)
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| Challenge: | In this paper, we evaluate use of different attribution methods for aiding identification of training data artifacts. |
| Approach: | They propose hybrid methods that combine saliency maps and instance attribution methods to aid in identifying training data artifacts. |
| Outcome: | The proposed methods can be used to efficiently uncover artifacts in training data when a challenging validation set is available. |
Permutation Invariant Strategy Using Transformer Encoders for Table Understanding (2022.findings-naacl)
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| Challenge: | Existing methods for encoding text in tables require additional training and require additional pretraining. |
| Approach: | They propose a novel encoding strategy that preserves the critical property of permutation invariance across rows or columns. |
| Outcome: | The proposed approach outperforms state-of-the-art methods on three table interpretation tasks: column type annotation, relation extraction, and entity linking. |
Improving Tool Retrieval by Leveraging Large Language Models for Query Generation (2025.coling-industry)
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| Challenge: | Large Language Models (LLMs) have shown great promise in common sense language understanding, conversational fluency, and reasoning. |
| Approach: | They propose to use Large Language Models to generate a retrieval query and embed it into the prompt to find relevant tools via a nearest-neighbor search. |
| Outcome: | The proposed method improves retrieval for in-domain (seen tools) and out-of-domain settings. |
DQA: Diagnostic Question Answering for IT Support (2026.acl-industry)
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Vishaal Kapoor, Mariam Dundua, Evren Yortucboylu, Sarthak Ahuja, Neda Kordjazi, Yiming Li, Vaibhavi padala, Derek Ho, Jennifer Whitted, Rebecca Steinert
| Challenge: | a diagnostic question-answering framework maintains persistent diagnostic state and aggregates retrieved cases at the level of root causes rather than individual documents. |
| Approach: | They propose a diagnostic question-answering framework that maintains persistent diagnostic state . it aggregates retrieved cases at the level of root causes rather than individual documents . |
| Outcome: | The framework achieves a 78.7% success rate under trajectory-level success criterion compared to a multi-turn RAG baseline . the framework reduces average turns from 8.4 to 3.9, compared with a single-turn baseline crim. |
HatePRISM: Policies, Platforms, and Research Integration. Advancing NLP for Hate Speech Proactive Mitigation (2025.findings-acl)
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Naquee Rizwan, Seid Muhie Yimam, Daryna Dementieva, Dr. Florian Skupin, Tim Fischer, Daniil Moskovskiy, Aarushi Ajay Borkar, Robert Geislinger, Punyajoy Saha, Sarthak Roy, Martin Semmann, Alexander Panchenko, Chris Biemann, Animesh Mukherjee
| Challenge: | Existing approaches to manage hate speech rely on reactive measures such as blocking or suspending offensive messages . despite regulations imposed by nations and social media platforms, hateful content remains a challenge . |
| Approach: | They propose a framework for automated hate speech moderation based on different strategies . they examine hate speech regulations and strategies from three perspectives . |
| Outcome: | The proposed framework could be based on a combination of country regulations, social platform policies, and NLP research datasets. |
Attention is not Explanation (N19-1)
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| Challenge: | Attention mechanisms have seen wide adoption in neural NLP models. |
| Approach: | They perform extensive experiments to assess the degree to which attention weights provide meaningful "explanations" they find that attention weighted inputs are often uncorrelated with gradient-based measures of feature importance . |
| Outcome: | The proposed model is based on a distribution over attended-to input units . the findings show that attention weights are often uncorrelated with features . |
ERASER: A Benchmark to Evaluate Rationalized NLP Models (2020.acl-main)
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Jay DeYoung, Sarthak Jain, Nazneen Fatema Rajani, Eric Lehman, Caiming Xiong, Richard Socher, Byron C. Wallace
| Challenge: | State-of-the-art models in NLP are opaque in terms of how they come to make predictions. |
| Approach: | They propose to release a benchmark to measure the quality of rationales extracted by models and how faithful these rationale are to human annotators. |
| Outcome: | The proposed benchmark will enable researchers to compare models and track progress on interpretable models for NLP. |
Taxonomy Construction of Unseen Domains via Graph-based Cross-Domain Knowledge Transfer (2020.acl-main)
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| Challenge: | Existing taxonomies are either entirely absent or missing. |
| Approach: | They propose a GNN-based cross-domain transfer framework for the taxonomy construction task. |
| Outcome: | The proposed framework improves on benchmark datasets from science and environment domains. |
Aligning to Constraints for Data-Efficient Language Model Customization (2025.findings-naacl)
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Fei Wang, Chao Shang, Shuai Wang, Sarthak Jain, Qiang Ning, Bonan Min, Vittorio Castelli, Yassine Benajiba, Dan Roth
| Challenge: | General-purpose language models (LMs) are aligned to diverse user intents, but fall short when it comes to specific applications. |
| Approach: | They propose a framework that uses constraints to automatically produce supervision signals for user alignment with constraints. |
| Outcome: | The proposed framework can produce supervision signals for user alignment with constraints. |
How Many and Which Training Points Would Need to be Removed to Flip this Prediction? (2023.eacl-main)
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| Challenge: | Existing methods to find St using brute-force are intractable. |
| Approach: | They propose a fast approximation method to find St based on influence functions . they propose to identify a minimum subset of training data that one would need to remove . |
| Outcome: | The proposed method can find St based on influence functions for simple classification models. |
Automatic Taxonomy Induction and Expansion (D19-3)
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Nicolas Rodolfo Fauceglia, Alfio Gliozzo, Sarthak Dash, Md. Faisal Mahbub Chowdhury, Nandana Mihindukulasooriya
| Challenge: | Knowledge Graph Induction Service (KGIS) enables automatic taxonomy induction and human-in-the-loop curation. |
| Approach: | They describe the features of the Knowledge Graph Induction Service (KGIS) KGIS allows the user to semi-automatically curate and expand the induced taxonomies through a component called Smart SpreadSheet . |
| Outcome: | The Knowledge Graph Induction Service (KGIS) is an end-to-end knowledge graph induction system. |
Scalable and Safe Remediation of Defective Actions in Self-Learning Conversational Systems (2023.acl-industry)
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| Challenge: | Off-Policy reinforcement learning has been used to improve conversational AIs, but in large-scale commercial environments it is challenging to balance between policy improvements and experience continuity. |
| Approach: | They propose to curate and leverage regression incident reports to validate, safe-guard, and improve policies prior to the online deployment. |
| Outcome: | The proposed method validates, safe-guards, and improves policies prior to the online deployment. |
Mitigating Hallucinated Translations in Large Language Models with Hallucination-focused Preference Optimization (2025.naacl-long)
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| Challenge: | Machine Translation (MT) systems based on fine-tuned large language models (LLMs) are at a higher risk of generating hallucinations, which can severely undermine user’s trust and safety. |
| Approach: | They propose a method that intrinsically learns to mitigate hallucinations during the model training phase. |
| Outcome: | The proposed method reduces hallucinations by 89% on an average across three unseen target languages while preserving translation quality. |
Leveraging Synthetic Targets for Machine Translation (2023.findings-acl)
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| Challenge: | Using synthetic target data, training models on synthetic targets outperforms training on actual ground-truth data. |
| Approach: | They propose a recipe for training machine translation models on synthetic target data by leveraging a large pre-trained model. |
| Outcome: | The proposed model outperforms training on real-world translation datasets. |
Open Knowledge Graphs Canonicalization using Variational Autoencoders (2021.emnlp-main)
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| Challenge: | Existing approaches to solve this problem generate embeddings for noun and relation phrases . ambiguous subject-relation-object triples are created by open knowledge graphs . |
| Approach: | They propose a model to learn both embeddings and cluster assignments in an end-to-end approach . they propose CUVA to be able to group noun and relation phrases using embeddable features . |
| Outcome: | The proposed model outperforms state-of-the-art methods over multiple benchmarks. |
ClaimPortal: Integrated Monitoring, Searching, Checking, and Analytics of Factual Claims on Twitter (P19-3)
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Sarthak Majithia, Fatma Arslan, Sumeet Lubal, Damian Jimenez, Priyank Arora, Josue Caraballo, Chengkai Li
| Challenge: | ClaimPortal is a web-based platform for monitoring, searching, checking and analyzing factual claims on Twitter from the American political domain. |
| Approach: | They present a web-based platform for monitoring, searching, checking and analyzing English factual claims on Twitter from the American political domain. |
| Outcome: | The proposed platform can monitor, search, check, and analyze English factual claims on Twitter from the political domain. |
Probing LLMs for hate speech detection: strengths and vulnerabilities (2023.findings-emnlp)
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| Challenge: | Recent efforts to detect hateful or toxic language using large language models have not used explanation, additional context and victim community information in the detection process. |
| Approach: | They use different prompt variations, input information and victim community information to evaluate large language models in zero shot setting without adding any in-context examples. |
| Outcome: | The proposed models perform significantly better when included in the pipeline than baseline models. |
GunStance: Stance Detection for Gun Control and Gun Regulation (2024.acl-long)
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Nikesh Gyawali, Iustin Sirbu, Tiberiu Sosea, Sarthak Khanal, Doina Caragea, Traian Rebedea, Cornelia Caragea
| Challenge: | Social media, especially Twitter, has been a melting pot for such debates. |
| Approach: | They propose to annotate tweets relevant to shooting events into three classes: In-Favor, Against, and Neutral. |
| Outcome: | The proposed approach outperforms supervised, semi-supervised, and LLM-based zero-shot models on the dataset. |
Compute-Efficient Churn Reduction for Conversational Agents (2023.emnlp-industry)
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| Challenge: | churn occurs when retraining models yields different predictions despite using the same data and hyper-parameters. |
| Approach: | They propose a method that pairs semantic parses based on their “function call signature” and encourages similarity through an additional loss based upon Jensen-Shannon Divergence. |
| Outcome: | The proposed method improves in academic, noisy, and industry settings. |
Bilingual Lexicon Induction with Semi-supervision in Non-Isometric Embedding Spaces (P19-1)
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| Challenge: | Recent work on bilingual lexicon induction (BLI) relies on an assumption about the isometry of two embedding spaces. |
| Approach: | They propose a semi-supervised approach that relaxes the isometric assumption while leveraging limited aligned bilingual lexicons and a larger set of unaligned word embeddings. |
| Outcome: | The proposed method obtains state-of-the-art results on 15 of 18 language pairs on the MUSE dataset and does particularly well when the embedding spaces don’t appear isometric. |
SUPP.AI: finding evidence for supplement-drug interactions (2020.acl-demos)
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Lucy Lu Wang, Oyvind Tafjord, Arman Cohan, Sarthak Jain, Sam Skjonsberg, Carissa Schoenick, Nick Botner, Waleed Ammar
| Challenge: | Dietary supplements are used by a large portion of the population, but information on their pharmacologic interactions is incomplete. |
| Approach: | They propose an application to search evidence sentences extracted from the literature to identify supplement-drug interactions. |
| Outcome: | The proposed model extracts supplement information and identifies interactions using labeled DDI data. |
Modular Self-Supervision for Document-Level Relation Extraction (2021.emnlp-main)
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| Challenge: | Prior work on information extraction tends to focus on binary relations within sentences . practical applications often require extracting complex relations across large text spans . |
| Approach: | They propose to decompose document-level relation extraction into relation detection and argument resolution, taking inspiration from Davidsonian semantics. |
| Outcome: | The proposed method outperforms state-of-the-art methods in biomedical machine reading for precision oncology by 20 absolute F1 points. |