51 citations · 149 across the 19 of their papers we have counts for
21 papers
CT: Counterfactual Causal Reasoning for Sentiment Shifts in Social-Media Conversation Trees
S M Rafiuddin, Atriya Sen
Sentiment in social-media threads does not only vary across posts; it shifts as users react to claims, corrections, evidence, and hostility within a branching reply tree. We study…
Mufu: Multilingual Fused Learning for Low-Resource Translation with LLM
Zheng Wei Lim, Nitish Gupta, Honglin Yu +1
Multilingual large language models (LLMs) are great translators, but this is largely limited to high-resource languages. For many LLMs, translating in and out of low-resource langu…
IndicGenBench: A Multilingual Benchmark to Evaluate Generation Capabilities of LLMs on Indic Languages
Harman Singh, Nitish Gupta, Shikhar Bharadwaj +2
As large language models (LLMs) see increasing adoption across the globe, it is imperative for LLMs to be representative of the linguistic diversity of the world. India is a lingui…
Potential-Based Reward Shaping For Intrinsic Motivation
Grant C. Forbes, Nitish Gupta, Leonardo Villalobos-Arias +3
Recently there has been a proliferation of intrinsic motivation (IM) reward-shaping methods to learn in complex and sparse-reward environments. These methods can often inadvertentl…
LLM Augmented LLMs: Expanding Capabilities through Composition
Rachit Bansal, Bidisha Samanta, Siddharth Dalmia +6
Foundational models with billions of parameters which have been trained on large corpora of data have demonstrated non-trivial skills in a variety of domains. However, due to their…
XTREME-UP: A User-Centric Scarce-Data Benchmark for Under-Represented Languages
Sebastian Ruder, Jonathan H. Clark, Alexander Gutkin +24
Data scarcity is a crucial issue for the development of highly multilingual NLP systems. Yet for many under-represented languages (ULs) -- languages for which NLP re-search is part…
Bootstrapping Multilingual Semantic Parsers using Large Language Models
Abhijeet Awasthi, Nitish Gupta, Bidisha Samanta +3
Despite cross-lingual generalization demonstrated by pre-trained multilingual models, the translate-train paradigm of transferring English datasets across multiple languages remain…
QA Is the New KR: Question-Answer Pairs as Knowledge Bases
Wenhu Chen, William W. Cohen, Michiel De Jong +4
In this position paper, we propose a new approach to generating a type of knowledge base (KB) from text, based on question generation and entity linking. We argue that the proposed…
Event Linking: Grounding Event Mentions to Wikipedia
Xiaodong Yu, Wenpeng Yin, Nitish Gupta +1
Comprehending an article requires understanding its constituent events. However, the context where an event is mentioned often lacks the details of this event. A question arises: h…
Enforcing Consistency in Weakly Supervised Semantic Parsing
Nitish Gupta, Sameer Singh, Matt Gardner
The predominant challenge in weakly supervised semantic parsing is that of spurious programs that evaluate to correct answers for the wrong reasons. Prior work uses elaborate searc…
Paired Examples as Indirect Supervision in Latent Decision Models
Nitish Gupta, Sameer Singh, Matt Gardner +1
Compositional, structured models are appealing because they explicitly decompose problems and provide interpretable intermediate outputs that give confidence that the model is not…
What do we expect from Multiple-choice QA Systems?
Krunal Shah, Nitish Gupta, Dan Roth
The recent success of machine learning systems on various QA datasets could be interpreted as a significant improvement in models' language understanding abilities. However, using…
Improving Compositional Generalization in Semantic Parsing
Inbar Oren, Jonathan Herzig, Nitish Gupta +2
Generalization of models to out-of-distribution (OOD) data has captured tremendous attention recently. Specifically, compositional generalization, i.e., whether a model generalizes…
Obtaining Faithful Interpretations from Compositional Neural Networks
Sanjay Subramanian, Ben Bogin, Nitish Gupta +4
Neural module networks (NMNs) are a popular approach for modeling compositionality: they achieve high accuracy when applied to problems in language and vision, while reflecting the…
Overestimation of Syntactic Representationin Neural Language Models
Jordan Kodner, Nitish Gupta
With the advent of powerful neural language models over the last few years, research attention has increasingly focused on what aspects of language they represent that make them so…
Evaluating Models' Local Decision Boundaries via Contrast Sets
Matt Gardner, Yoav Artzi, Victoria Basmova +23
Standard test sets for supervised learning evaluate in-distribution generalization. Unfortunately, when a dataset has systematic gaps (e.g., annotation artifacts), these evaluation…
Robust Named Entity Recognition with Truecasing Pretraining
Stephen Mayhew, Nitish Gupta, Dan Roth
Although modern named entity recognition (NER) systems show impressive performance on standard datasets, they perform poorly when presented with noisy data. In particular, capitali…
Neural Module Networks for Reasoning over Text
Nitish Gupta, Kevin Lin, Dan Roth +2
Answering compositional questions that require multiple steps of reasoning against text is challenging, especially when they involve discrete, symbolic operations. Neural module ne…
Joint Multilingual Supervision for Cross-lingual Entity Linking
Shyam Upadhyay, Nitish Gupta, Dan Roth
Cross-lingual Entity Linking (XEL) aims to ground entity mentions written in any language to an English Knowledge Base (KB), such as Wikipedia. XEL for most languages is challengin…
Neural Compositional Denotational Semantics for Question Answering
Nitish Gupta, Mike Lewis
Answering compositional questions requiring multi-step reasoning is challenging. We introduce an end-to-end differentiable model for interpreting questions about a knowledge graph…
Collectively Embedding Multi-Relational Data for Predicting User Preferences
Nitish Gupta, Sameer Singh
Matrix factorization has found incredible success and widespread application as a collaborative filtering based approach to recommendations. Unfortunately, incorporating additional…