Papers by Nazneen Fatema Rajani
GeDi: Generative Discriminator Guided Sequence Generation (2021.findings-emnlp)
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Ben Krause, Akhilesh Deepak Gotmare, Bryan McCann, Nitish Shirish Keskar, Shafiq Joty, Richard Socher, Nazneen Fatema Rajani
| Challenge: | Existing methods for controlling LMs have limitations. |
| Approach: | They propose a class-conditional LM that uses a control code to control text generation. |
| Outcome: | The proposed algorithm is much faster than the existing methods for generating from the LM directly. |
Goodwill Hunting: Analyzing and Repurposing Off-the-Shelf Named Entity Linking Systems (2021.naacl-industry)
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| Challenge: | Named entity linking (NEL) is a preprocessing step in commercial systems . a small organization or individual could use an off-the-shelf system to accomplish the same objectives . |
| Approach: | They examine how to repurpose off-the-shelf NEL systems to correct sport-related errors. |
| Outcome: | The proposed model can improve sports question-answering accuracy by 25% . the proposed model is based on the best available model . |
DART: Open-Domain Structured Data Record to Text Generation (2021.naacl-main)
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Linyong Nan, Dragomir Radev, Rui Zhang, Amrit Rau, Abhinand Sivaprasad, Chiachun Hsieh, Xiangru Tang, Aadit Vyas, Neha Verma, Pranav Krishna, Yangxiaokang Liu, Nadia Irwanto, Jessica Pan, Faiaz Rahman, Ahmad Zaidi, Mutethia Mutuma, Yasin Tarabar, Ankit Gupta, Tao Yu, Yi Chern Tan, Xi Victoria Lin, Caiming Xiong, Richard Socher, Nazneen Fatema Rajani
| Challenge: | Data-to-text annotations can be costly when dealing with tables with nontrivial structures. |
| Approach: | They propose a procedure for extracting semantic triples from tables that encodes their structures by exploiting table headers and table title. |
| Outcome: | The proposed method exploits the semantic dependencies between table headers and title to extract semantic triples from tables. |
Robustness Gym: Unifying the NLP Evaluation Landscape (2021.naacl-demos)
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| Challenge: | Existing tools cater to specialized set of evaluations and provide no clear way to leverage or share findings from prior evaluations. |
| Approach: | They propose a toolkit that unifies 4 evaluation paradigms to provide a common platform for evaluation. |
| Outcome: | The proposed evaluation toolkit unifies 4 evaluation paradigms and is under active development. |
Stacking with Auxiliary Features for Visual Question Answering (N18-1)
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| Challenge: | Visual Question Answering (VQA) is a challenging task that requires systems to reason about natural language and vision. |
| Approach: | They propose four categories of auxiliary features for ensembling for VQA . three out of the four categories can be inferred from an image-question pair . fourth category uses model-specific explanations . |
| Outcome: | The proposed techniques improve performance for visual question answering (VQA) given an image and a natural language question, the task is to provide an accurate natural language answer. |
ESPRIT: Explaining Solutions to Physical Reasoning Tasks (2020.acl-main)
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Nazneen Fatema Rajani, Rui Zhang, Yi Chern Tan, Stephan Zheng, Jeremy Weiss, Aadit Vyas, Abhijit Gupta, Caiming Xiong, Richard Socher, Dragomir Radev
| Challenge: | Neural networks lack the ability to reason about qualitative physics and cannot generalize to scenarios and tasks unseen during training. |
| Approach: | They propose a framework for reasoning about qualitative physics in natural language that generates interpretable descriptions of physical events. |
| Outcome: | The proposed framework generates explanations of how the physical simulation will causally evolve so that an agent or a human can reason about a solution using interpretable descriptions. |
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. |
Universal Natural Language Processing with Limited Annotations: Try Few-shot Textual Entailment as a Start (2020.emnlp-main)
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| Challenge: | a current approach to solving NLP problems is to build a problem-specific dataset . current approaches do not allow for transforming tasks into textual entailment . |
| Approach: | They propose a pretrained textual entailment system that can generalize across domains . they argue that when is it worth transforming an NLP task into textual detailment? |
| Outcome: | The proposed model can generalize across domains with few examples, the authors argue . they show that it can be used for several downstream NLP tasks with limited annotations . |
Double-Hard Debias: Tailoring Word Embeddings for Gender Bias Mitigation (2020.acl-main)
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| Challenge: | Existing methods to debias word embeddings from human-generated corpora inherit strong gender bias . prior work has suggested removing gender component from pre-trained word embeds or compressing gender information into a few dimensions of the embeddable space . |
| Approach: | They propose a technique that purifies word embeddings against inferred gender subspaces . they propose to preserve distributional semantics of pre-trained word embeds while reducing gender bias . |
| Outcome: | The proposed technique preserves distributional semantics of pre-trained word embeddings while reducing gender bias to a larger degree than prior approaches. |
Explain Yourself! Leveraging Language Models for Commonsense Reasoning (P19-1)
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| Challenge: | Empirical results indicate that we can effectively leverage language models for commonsense reasoning. |
| Approach: | They propose to use commonsense auto-generated explanations to train language models to generate explanations that can be used during training and inference in a commonsensense Auto-Generated Explanation framework. |
| Outcome: | Empirical results show that the proposed framework improves on the commonsenseQA task by 10%. |