Papers by Chaitanya Malaviya
Neural Factor Graph Models for Cross-lingual Morphological Tagging (P18-1)
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| Challenge: | Existing approaches to morphological tagging are limited by the assumption that tag sets overlap . a limited amount of data is available for most languages to learn these morphology taggers. |
| Approach: | They propose a method for cross-lingual morphological tagging that relaxes this assumption . they use factorial conditional random fields with neural network potentials to smooth over superficial differences in the surface forms . |
| Outcome: | The proposed model can smooth over superficial differences in the surface forms and generate unseen or rare tag sets. |
Generative Data Augmentation for Commonsense Reasoning (2020.findings-emnlp)
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Yiben Yang, Chaitanya Malaviya, Jared Fernandez, Swabha Swayamdipta, Ronan Le Bras, Ji-Ping Wang, Chandra Bhagavatula, Yejin Choi, Doug Downey
| Challenge: | Recent advances in commonsense reasoning depend on large-scale human-authored training data. |
| Approach: | They propose a generative data augmentation technique that augments human-authored training data by using pretrained language models. |
| Outcome: | The proposed technique outperforms existing methods on commonsense reasoning benchmarks and enhances out-of-distribution generalization. |
Dolomites: Domain-Specific Long-Form Methodical Tasks (2025.tacl-1)
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Chaitanya Malaviya, Priyanka Agrawal, Kuzman Ganchev, Pranesh Srinivasan, Fantine Huot, Jonathan Berant, Mark Yatskar, Dipanjan Das, Mirella Lapata, Chris Alberti
| Challenge: | Experts in various fields perform methodical writing tasks to plan, organize, and report their work. |
| Approach: | They propose a benchmark with specifications for 519 methodical writing tasks . they use expert revisions of up to 10 model-generated examples to evaluate contemporary language models. |
| Outcome: | The proposed benchmark includes specifications for 519 methodical writing tasks . it includes examples with input and output examples, and is available at https://dolomites-benchmark.github.io/ . |
QUEST: A Retrieval Dataset of Entity-Seeking Queries with Implicit Set Operations (2023.acl-long)
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| Challenge: | People express information needs with multiple preferences or constraints . modern retrieval systems struggle on such queries, a study finds . |
| Approach: | They construct a dataset of 3357 queries that map to a set of Wikipedia entities . they use crowd-sourced data to match constraints with evidence in documents . |
| Outcome: | The proposed dataset challenges models to match constraints mentioned in queries with evidence in documents and correctly perform various set operations. |
COMET: Commonsense Transformers for Automatic Knowledge Graph Construction (P19-1)
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| Challenge: | Existing studies on commonsense knowledge base construction only store loosely structured open-text descriptions of knowledge. |
| Approach: | They propose a commonsense knowledge base construction model that generates rich commonsensense descriptions in natural language. |
| Outcome: | The proposed models can generate rich and diverse commonsense descriptions in natural language. |
What if you said that differently?: How Explanation Formats Affect Human Feedback Efficacy and User Perception (2024.naacl-long)
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| Challenge: | Question answering models can often be black boxes, as their reasoning process is mostly opaque. |
| Approach: | They analyze the effect of rationales generated by QA models on user feedback and how well they enable users to understand and trust model answers. |
| Outcome: | The proposed model can be used to improve model responses by removing feedback from end users and enhancing model outputs by using natural language feedback. |
AssistantBench: Can Web Agents Solve Realistic and Time-Consuming Tasks? (2024.emnlp-main)
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| Challenge: | Current language models and retrieval-augmented LMs are limited in their ability to perform tasks on the web. |
| Approach: | They propose a benchmark to evaluate language agents built on top of language models . they propose 'AssistantBench' which includes 214 tasks that can be automatically evaluated . |
| Outcome: | The proposed agent outperforms existing agents in a new benchmark for language agents on the web. |
A Simple Joint Model for Improved Contextual Neural Lemmatization (N19-1)
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| Challenge: | False positive: a core NLP task of lemmatization seeks to map multiple forms of English verbs to a canonical one, known as the lemma. |
| Approach: | They propose a joint neural model for lemmatization and morphological tagging that achieves state-of-the-art results on 20 languages from the Universal Dependencies corpora. |
| Outcome: | The proposed model achieves state-of-the-art results on 20 languages from the Universal Dependencies corpora. |
On Reference (In-)Determinacy in Natural Language Inference (2025.findings-naacl)
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Sihao Chen, Chaitanya Malaviya, Alex Fabrikant, Hagai Taitelbaum, Tal Schuster, Senaka Buthpitiya, Dan Roth
| Challenge: | Using reference determinacy, models fail to recognize context mismatch in NLI examples . |
| Approach: | They propose a benchmark to identify reference ambiguity in NLI examples . they propose RD as a possible assumption for natural language inference . |
| Outcome: | The proposed benchmark identifies reference ambiguity in natural language inference examples . 80% false contradiction and >50% entailment predictions are found . |
LogiCoL: Logically-Informed Contrastive Learning for Set-based Dense Retrieval (2025.emnlp-main)
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| Challenge: | Current dense retrievers struggle with queries with logical connectives, a use case that is often overlooked but important in downstream applications. |
| Approach: | They propose a logically-informed contrastive learning objective for dense retrievers that learns to respect the subset and mutually exclusive set relation between query results. |
| Outcome: | The proposed model improves retrieval performance and consistency on entity retrieval tasks. |
AmbiCoref: Evaluating Human and Model Sensitivity to Ambiguous Coreference (2023.findings-eacl)
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| Challenge: | Existing models do not have welldefined target behavior for coreferential ambiguity. |
| Approach: | They propose to use AmbiCoref to test whether coreference resolution models are sensitive to ambiguity. |
| Outcome: | The proposed model is more sensitive to ambiguity than existing models. |
Cascading Biases: Investigating the Effect of Heuristic Annotation Strategies on Data and Models (2022.emnlp-main)
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| Challenge: | Cognitive psychologists have documented that humans use cognitive heuristics to make quick decisions while expending less effort. |
| Approach: | They propose tracking annotator heuristic traces where they measure low-effort annotation strategies that could indicate usage of various cognitive heurs. |
| Outcome: | The proposed tracking annotator heuristic traces shows that annotators are using multiple cognitive heurs based on psychological tests. |
Sparse and Constrained Attention for Neural Machine Translation (P18-2)
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| Challenge: | Existing approaches to address coverage problem only change attention transformations . adequacy of neural machine translation is still a major concern . |
| Approach: | They propose a new approach that allocates fertilities to source words to bound attention . they propose gating architectures and adaptive attention control to control the amount of source context . |
| Outcome: | The proposed model is differentiable and sparse and is evaluated in three languages pairs. |
ExpertQA: Expert-Curated Questions and Attributed Answers (2024.naacl-long)
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| Challenge: | a recent study examined the attribution and factuality of language models in domains . experts from various fields are using large language models for information-seeking scenarios . |
| Approach: | They evaluate language models' attribution and factuality by bringing domain experts in the loop . they collect expert-curated questions from 484 participants across 32 fields of study . |
| Outcome: | The results show that language models can provide factually correct answers in high-stakes fields, but they can also be harmful to experts. |