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20152022
most citedHow Much Can CLIP Benefit Vision-and-Language Tasks?

153 citations · 601 across the 67 of their papers we have counts for

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Showing 2018Show all

16 papers · 1 filter

cs.CL2018

Combining Fact Extraction and Verification with Neural Semantic Matching Networks

Yixin Nie, Haonan Chen, Mohit Bansal

The increasing concern with misinformation has stimulated research efforts on automatic fact checking. The recently-released FEVER dataset introduced a benchmark fact-verification…

cs.CL2018

Analyzing Compositionality-Sensitivity of NLI Models

Yixin Nie, Yicheng Wang, Mohit Bansal

Success in natural language inference (NLI) should require a model to understand both lexical and compositional semantics. However, through adversarial evaluation, we find that sev…

cs.CL2018

Game-Based Video-Context Dialogue

Ramakanth Pasunuru, Mohit Bansal

Current dialogue systems focus more on textual and speech context knowledge and are usually based on two speakers. Some recent work has investigated static image-based dialogue. Ho…

cs.CL2018

SafeCity: Understanding Diverse Forms of Sexual Harassment Personal Stories

Sweta Karlekar, Mohit Bansal

With the recent rise of #MeToo, an increasing number of personal stories about sexual harassment and sexual abuse have been shared online. In order to push forward the fight agains…

cs.CL2018

Closed-Book Training to Improve Summarization Encoder Memory

Yichen Jiang, Mohit Bansal

A good neural sequence-to-sequence summarization model should have a strong encoder that can distill and memorize the important information from long input texts so that the decode…

cs.CL2018

Adversarial Over-Sensitivity and Over-Stability Strategies for Dialogue Models

Tong Niu, Mohit Bansal

We present two categories of model-agnostic adversarial strategies that reveal the weaknesses of several generative, task-oriented dialogue models: Should-Not-Change strategies tha…