44 citations · 76 across the 10 of their papers we have counts for
13 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…
Can Language Models Learn to Listen?
Evonne Ng, Sanjay Subramanian, Dan Klein +3
We present a framework for generating appropriate facial responses from a listener in dyadic social interactions based on the speaker's words. Given an input transcription of the s…
Modular Visual Question Answering via Code Generation
Sanjay Subramanian, Medhini Narasimhan, Kushal Khangaonkar +6
We present a framework that formulates visual question answering as modular code generation. In contrast to prior work on modular approaches to VQA, our approach requires no additi…
ReCLIP: A Strong Zero-Shot Baseline for Referring Expression Comprehension
Sanjay Subramanian, William Merrill, Trevor Darrell +3
Training a referring expression comprehension (ReC) model for a new visual domain requires collecting referring expressions, and potentially corresponding bounding boxes, for image…
MedICaT: A Dataset of Medical Images, Captions, and Textual References
Sanjay Subramanian, Lucy Lu Wang, Sachin Mehta +6
Understanding the relationship between figures and text is key to scientific document understanding. Medical figures in particular are quite complex, often consisting of several su…
Latent Compositional Representations Improve Systematic Generalization in Grounded Question Answering
Ben Bogin, Sanjay Subramanian, Matt Gardner +1
Answering questions that involve multi-step reasoning requires decomposing them and using the answers of intermediate steps to reach the final answer. However, state-of-the-art mod…
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…
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…
AllenNLP Interpret: A Framework for Explaining Predictions of NLP Models
Eric Wallace, Jens Tuyls, Junlin Wang +3
Neural NLP models are increasingly accurate but are imperfect and opaque---they break in counterintuitive ways and leave end users puzzled at their behavior. Model interpretation m…
An Improved Neural Baseline for Temporal Relation Extraction
Qiang Ning, Sanjay Subramanian, Dan Roth
Determining temporal relations (e.g., before or after) between events has been a challenging natural language understanding task, partly due to the difficulty to generate large amo…
Correlation Clustering with Same-Cluster Queries Bounded by Optimal Cost
Barna Saha, Sanjay Subramanian
Several clustering frameworks with interactive (semi-supervised) queries have been studied in the past. Recently, clustering with same-cluster queries has become popular. An algori…
Improving Generalization in Coreference Resolution via Adversarial Training
Sanjay Subramanian, Dan Roth
In order for coreference resolution systems to be useful in practice, they must be able to generalize to new text. In this work, we demonstrate that the performance of the state-of…
Named Person Coreference in English News
Oshin Agarwal, Sanjay Subramanian, Ani Nenkova +1
People are often entities of interest in tasks such as search and information extraction. In these tasks, the goal is to find as much information as possible about people specified…