Search Results for author: Nazneen Fatema Rajani

Found 23 papers, 11 papers with code

MoFE: Mixture of Factual Experts for Controlling Hallucinations in Abstractive Summarization

no code implementations14 Oct 2021 Prafulla Kumar Choubey, Jesse Vig, Wenhao Liu, Nazneen Fatema Rajani

We train our experts using reinforcement learning (RL) to minimize the error defined by two factual consistency metrics: entity overlap and dependency arc entailment.

Abstractive Text Summarization

HydraSum -- Disentangling Stylistic Features in Text Summarization using Multi-Decoder Models

1 code implementation8 Oct 2021 Tanya Goyal, Nazneen Fatema Rajani, Wenhao Liu, Wojciech Kryściński

Existing abstractive summarization models lack explicit control mechanisms that would allow users to influence the stylistic features of the model outputs.

Abstractive Text Summarization

SummVis: Interactive Visual Analysis of Models, Data, and Evaluation for Text Summarization

1 code implementation ACL 2021 Jesse Vig, Wojciech Kryściński, Karan Goel, Nazneen Fatema Rajani

Novel neural architectures, training strategies, and the availability of large-scale corpora haven been the driving force behind recent progress in abstractive text summarization.

Abstractive Text Summarization

FastIF: Scalable Influence Functions for Efficient Model Interpretation and Debugging

1 code implementation31 Dec 2020 Han Guo, Nazneen Fatema Rajani, Peter Hase, Mohit Bansal, Caiming Xiong

With the availability of the fast influence functions, we demonstrate their usefulness in four applications.

Data Augmentation

Profile Prediction: An Alignment-Based Pre-Training Task for Protein Sequence Models

no code implementations1 Dec 2020 Pascal Sturmfels, Jesse Vig, Ali Madani, Nazneen Fatema Rajani

Recent deep-learning approaches to protein prediction have shown that pre-training on unlabeled data can yield useful representations for downstream tasks.

Language Modelling

Explaining and Improving Model Behavior with k Nearest Neighbor Representations

no code implementations18 Oct 2020 Nazneen Fatema Rajani, Ben Krause, Wengpeng Yin, Tong Niu, Richard Socher, Caiming Xiong

Interpretability techniques in NLP have mainly focused on understanding individual predictions using attention visualization or gradient-based saliency maps over tokens.

Natural Language Inference

Explaining Creative Artifacts

no code implementations14 Oct 2020 Lav R. Varshney, Nazneen Fatema Rajani, Richard Socher

Human creativity is often described as the mental process of combining associative elements into a new form, but emerging computational creativity algorithms may not operate in this manner.

Text Generation Traveling Salesman Problem

ReviewRobot: Explainable Paper Review Generation based on Knowledge Synthesis

1 code implementation13 Oct 2020 Qingyun Wang, Qi Zeng, Lifu Huang, Kevin Knight, Heng Ji, Nazneen Fatema Rajani

To assist human review process, we build a novel ReviewRobot to automatically assign a review score and write comments for multiple categories such as novelty and meaningful comparison.

Review Generation

Universal Natural Language Processing with Limited Annotations: Try Few-shot Textual Entailment as a Start

1 code implementation EMNLP 2020 Wenpeng Yin, Nazneen Fatema Rajani, Dragomir Radev, Richard Socher, Caiming Xiong

We demonstrate that this framework enables a pretrained entailment model to work well on new entailment domains in a few-shot setting, and show its effectiveness as a unified solver for several downstream NLP tasks such as question answering and coreference resolution when the end-task annotations are limited.

Coreference Resolution Natural Language Inference +1

GeDi: Generative Discriminator Guided Sequence Generation

2 code implementations14 Sep 2020 Ben Krause, Akhilesh Deepak Gotmare, Bryan McCann, Nitish Shirish Keskar, Shafiq Joty, Richard Socher, Nazneen Fatema Rajani

While large-scale language models (LMs) are able to imitate the distribution of natural language well enough to generate realistic text, it is difficult to control which regions of the distribution they generate.

Linguistic Acceptability Word Embeddings

BERTology Meets Biology: Interpreting Attention in Protein Language Models

2 code implementations ICLR 2021 Jesse Vig, Ali Madani, Lav R. Varshney, Caiming Xiong, Richard Socher, Nazneen Fatema Rajani

Transformer architectures have proven to learn useful representations for protein classification and generation tasks.

Double-Hard Debias: Tailoring Word Embeddings for Gender Bias Mitigation

1 code implementation ACL 2020 Tianlu Wang, Xi Victoria Lin, Nazneen Fatema Rajani, Bryan McCann, Vicente Ordonez, Caiming Xiong

Word embeddings derived from human-generated corpora inherit strong gender bias which can be further amplified by downstream models.

Word Embeddings

ESPRIT: Explaining Solutions to Physical Reasoning Tasks

2 code implementations ACL 2020 Nazneen Fatema Rajani, Rui Zhang, Yi Chern Tan, Stephan Zheng, Jeremy Weiss, Aadit Vyas, Abhijit Gupta, Caiming Xiong, Richard Socher, Dragomir Radev

Our framework learns to generate explanations of how the physical simulation will causally evolve so that an agent or a human can easily reason about a solution using those interpretable descriptions.

ERASER: A Benchmark to Evaluate Rationalized NLP Models

no code implementations ACL 2020 Jay DeYoung, Sarthak Jain, Nazneen Fatema Rajani, Eric Lehman, Caiming Xiong, Richard Socher, Byron C. Wallace

We propose several metrics that aim to capture how well the rationales provided by models align with human rationales, and also how faithful these rationales are (i. e., the degree to which provided rationales influenced the corresponding predictions).

Explain Yourself! Leveraging Language Models for Commonsense Reasoning

1 code implementation ACL 2019 Nazneen Fatema Rajani, Bryan McCann, Caiming Xiong, Richard Socher

Deep learning models perform poorly on tasks that require commonsense reasoning, which often necessitates some form of world-knowledge or reasoning over information not immediately present in the input.

Common Sense Reasoning

Stacking With Auxiliary Features for Entity Linking in the Medical Domain

no code implementations WS 2017 Nazneen Fatema Rajani, Mihaela Bornea, Ken Barker

In the medical domain, it is common to link text spans to medical concepts in large, curated knowledge repositories such as the Unified Medical Language System.

Entity Linking

Supervised and Unsupervised Ensembling for Knowledge Base Population

no code implementations16 Apr 2016 Nazneen Fatema Rajani, Raymond J. Mooney

We present results on combining supervised and unsupervised methods to ensemble multiple systems for two popular Knowledge Base Population (KBP) tasks, Cold Start Slot Filling (CSSF) and Tri-lingual Entity Discovery and Linking (TEDL).

Knowledge Base Population Slot Filling

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