Search Results for author: Bhuwan Dhingra

Found 42 papers, 21 papers with code

Investigating the Effect of Background Knowledge on Natural Questions

no code implementations NAACL (DeeLIO) 2021 Vidhisha Balachandran, Bhuwan Dhingra, Haitian Sun, Michael Collins, William Cohen

We create a subset of the NQ data, Factual Questions (FQ), where the questions have evidence in the KB in the form of paths that link question entities to answer entities but still must be answered using text, to facilitate further research into KB integration methods.

Natural Questions Retrieval

Hierarchical Multi-Instance Multi-Label Learning for Detecting Propaganda Techniques

no code implementations30 May 2023 Anni Chen, Bhuwan Dhingra

Since the introduction of the SemEval 2020 Task 11 (Martino et al., 2020a), several approaches have been proposed in the literature for classifying propaganda based on the rhetorical techniques used to influence readers.

Multi-Label Learning

Selectively Answering Ambiguous Questions

no code implementations24 May 2023 Jeremy R. Cole, Michael J. Q. Zhang, Daniel Gillick, Julian Martin Eisenschlos, Bhuwan Dhingra, Jacob Eisenstein

We investigate question answering from this perspective, focusing on answering a subset of questions with a high degree of accuracy, from a set of questions in which many are inherently ambiguous.

Question Answering

Salient Span Masking for Temporal Understanding

no code implementations22 Mar 2023 Jeremy R. Cole, Aditi Chaudhary, Bhuwan Dhingra, Partha Talukdar

First, we find that SSM alone improves the downstream performance on three temporal tasks by an avg.

Avg Language Modelling +1

Learning the Legibility of Visual Text Perturbations

1 code implementation9 Mar 2023 Dev Seth, Rickard Stureborg, Danish Pruthi, Bhuwan Dhingra

In this work, we address this gap by learning models that predict the legibility of a perturbed string, and rank candidate perturbations based on their legibility.

DIFFQG: Generating Questions to Summarize Factual Changes

no code implementations1 Mar 2023 Jeremy R. Cole, Palak Jain, Julian Martin Eisenschlos, Michael J. Q. Zhang, Eunsol Choi, Bhuwan Dhingra

We propose representing factual changes between paired documents as question-answer pairs, where the answer to the same question differs between two versions.

Change Detection Question Generation +1

On the State of the Art in Authorship Attribution and Authorship Verification

1 code implementation14 Sep 2022 Jacob Tyo, Bhuwan Dhingra, Zachary C. Lipton

Despite decades of research on authorship attribution (AA) and authorship verification (AV), inconsistent dataset splits/filtering and mismatched evaluation methods make it difficult to assess the state of the art.

Authorship Verification

Characterizing the Efficiency vs. Accuracy Trade-off for Long-Context NLP Models

1 code implementation nlppower (ACL) 2022 Phyllis Ang, Bhuwan Dhingra, Lisa Wu Wills

In this work, we perform a systematic study of this accuracy vs. efficiency trade-off on two widely used long-sequence models - Longformer-Encoder-Decoder (LED) and Big Bird - during fine-tuning and inference on four datasets from the SCROLLS benchmark.

Playing the Game of 2048 Question Answering

ASQA: Factoid Questions Meet Long-Form Answers

no code implementations12 Apr 2022 Ivan Stelmakh, Yi Luan, Bhuwan Dhingra, Ming-Wei Chang

In contrast to existing long-form QA tasks (such as ELI5), ASQA admits a clear notion of correctness: a user faced with a good summary should be able to answer different interpretations of the original ambiguous question.

Question Answering

Time-Aware Language Models as Temporal Knowledge Bases

no code implementations29 Jun 2021 Bhuwan Dhingra, Jeremy R. Cole, Julian Martin Eisenschlos, Daniel Gillick, Jacob Eisenstein, William W. Cohen

We introduce a diagnostic dataset aimed at probing LMs for factual knowledge that changes over time and highlight problems with LMs at either end of the spectrum -- those trained on specific slices of temporal data, as well as those trained on a wide range of temporal data.


Fool Me Twice: Entailment from Wikipedia Gamification

1 code implementation NAACL 2021 Julian Martin Eisenschlos, Bhuwan Dhingra, Jannis Bulian, Benjamin Börschinger, Jordan Boyd-Graber

We release FoolMeTwice (FM2 for short), a large dataset of challenging entailment pairs collected through a fun multi-player game.


Reasoning Over Virtual Knowledge Bases With Open Predicate Relations

no code implementations14 Feb 2021 Haitian Sun, Pat Verga, Bhuwan Dhingra, Ruslan Salakhutdinov, William W. Cohen

We present the Open Predicate Query Language (OPQL); a method for constructing a virtual KB (VKB) trained entirely from text.

Language Modelling Open-Domain Question Answering

Evaluating Explanations: How much do explanations from the teacher aid students?

1 code implementation1 Dec 2020 Danish Pruthi, Rachit Bansal, Bhuwan Dhingra, Livio Baldini Soares, Michael Collins, Zachary C. Lipton, Graham Neubig, William W. Cohen

While many methods purport to explain predictions by highlighting salient features, what aims these explanations serve and how they ought to be evaluated often go unstated.

Question Answering text-classification +1

Weakly- and Semi-supervised Evidence Extraction

1 code implementation Findings of the Association for Computational Linguistics 2020 Danish Pruthi, Bhuwan Dhingra, Graham Neubig, Zachary C. Lipton

For many prediction tasks, stakeholders desire not only predictions but also supporting evidence that a human can use to verify its correctness.

Differentiable Open-Ended Commonsense Reasoning

no code implementations NAACL 2021 Bill Yuchen Lin, Haitian Sun, Bhuwan Dhingra, Manzil Zaheer, Xiang Ren, William W. Cohen

As a step towards making commonsense reasoning research more realistic, we propose to study open-ended commonsense reasoning (OpenCSR) -- the task of answering a commonsense question without any pre-defined choices -- using as a resource only a corpus of commonsense facts written in natural language.


ToTTo: A Controlled Table-To-Text Generation Dataset

1 code implementation EMNLP 2020 Ankur P. Parikh, Xuezhi Wang, Sebastian Gehrmann, Manaal Faruqui, Bhuwan Dhingra, Diyi Yang, Dipanjan Das

We present ToTTo, an open-domain English table-to-text dataset with over 120, 000 training examples that proposes a controlled generation task: given a Wikipedia table and a set of highlighted table cells, produce a one-sentence description.

Conditional Text Generation Data-to-Text Generation +1

Differentiable Reasoning over a Virtual Knowledge Base

1 code implementation ICLR 2020 Bhuwan Dhingra, Manzil Zaheer, Vidhisha Balachandran, Graham Neubig, Ruslan Salakhutdinov, William W. Cohen

In particular, we describe a neural module, DrKIT, that traverses textual data like a KB, softly following paths of relations between mentions of entities in the corpus.


Learning to Deceive with Attention-Based Explanations

3 code implementations ACL 2020 Danish Pruthi, Mansi Gupta, Bhuwan Dhingra, Graham Neubig, Zachary C. Lipton

Attention mechanisms are ubiquitous components in neural architectures applied to natural language processing.


Handling Divergent Reference Texts when Evaluating Table-to-Text Generation

1 code implementation ACL 2019 Bhuwan Dhingra, Manaal Faruqui, Ankur Parikh, Ming-Wei Chang, Dipanjan Das, William W. Cohen

Automatically constructed datasets for generating text from semi-structured data (tables), such as WikiBio, often contain reference texts that diverge from the information in the corresponding semi-structured data.

Table-to-Text Generation

Combating Adversarial Misspellings with Robust Word Recognition

3 code implementations ACL 2019 Danish Pruthi, Bhuwan Dhingra, Zachary C. Lipton

To combat adversarial spelling mistakes, we propose placing a word recognition model in front of the downstream classifier.

Sentiment Analysis

Probing Biomedical Embeddings from Language Models

1 code implementation WS 2019 Qiao Jin, Bhuwan Dhingra, William W. Cohen, Xinghua Lu

For this we use the pre-trained LMs as fixed feature extractors and restrict the downstream task models to not have additional sequence modeling layers.

NER Word Embeddings

GLoMo: Unsupervised Learning of Transferable Relational Graphs

no code implementations NeurIPS 2018 Zhilin Yang, Jake Zhao, Bhuwan Dhingra, Kaiming He, William W. Cohen, Ruslan R. Salakhutdinov, Yann Lecun

We also show that the learned graphs are generic enough to be transferred to different embeddings on which the graphs have not been trained (including GloVe embeddings, ELMo embeddings, and task-specific RNN hidden units), or embedding-free units such as image pixels.

Image Classification Natural Language Inference +4

Open Domain Question Answering Using Early Fusion of Knowledge Bases and Text

2 code implementations EMNLP 2018 Haitian Sun, Bhuwan Dhingra, Manzil Zaheer, Kathryn Mazaitis, Ruslan Salakhutdinov, William W. Cohen

In this paper we look at a more practical setting, namely QA over the combination of a KB and entity-linked text, which is appropriate when an incomplete KB is available with a large text corpus.

Graph Representation Learning Open-Domain Question Answering

GLoMo: Unsupervisedly Learned Relational Graphs as Transferable Representations

1 code implementation14 Jun 2018 Zhilin Yang, Jake Zhao, Bhuwan Dhingra, Kaiming He, William W. Cohen, Ruslan Salakhutdinov, Yann Lecun

We also show that the learned graphs are generic enough to be transferred to different embeddings on which the graphs have not been trained (including GloVe embeddings, ELMo embeddings, and task-specific RNN hidden unit), or embedding-free units such as image pixels.

Image Classification Natural Language Inference +4

Embedding Text in Hyperbolic Spaces

no code implementations WS 2018 Bhuwan Dhingra, Christopher J. Shallue, Mohammad Norouzi, Andrew M. Dai, George E. Dahl

Ideally, we could incorporate our prior knowledge of this hierarchical structure into unsupervised learning algorithms that work on text data.

Sentence Embeddings

Neural Models for Reasoning over Multiple Mentions using Coreference

no code implementations NAACL 2018 Bhuwan Dhingra, Qiao Jin, Zhilin Yang, William W. Cohen, Ruslan Salakhutdinov

Many problems in NLP require aggregating information from multiple mentions of the same entity which may be far apart in the text.


Simple and Effective Semi-Supervised Question Answering

no code implementations NAACL 2018 Bhuwan Dhingra, Danish Pruthi, Dheeraj Rajagopal

Recent success of deep learning models for the task of extractive Question Answering (QA) is hinged on the availability of large annotated corpora.

Extractive Question-Answering Question Answering +1

Question Answering from Unstructured Text by Retrieval and Comprehension

no code implementations26 Mar 2017 Yusuke Watanabe, Bhuwan Dhingra, Ruslan Salakhutdinov

Open domain Question Answering (QA) systems must interact with external knowledge sources, such as web pages, to find relevant information.

Open-Domain Question Answering Retrieval

Linguistic Knowledge as Memory for Recurrent Neural Networks

no code implementations7 Mar 2017 Bhuwan Dhingra, Zhilin Yang, William W. Cohen, Ruslan Salakhutdinov

We introduce a model that encodes such graphs as explicit memory in recurrent neural networks, and use it to model coreference relations in text.


A Comparative Study of Word Embeddings for Reading Comprehension

no code implementations2 Mar 2017 Bhuwan Dhingra, Hanxiao Liu, Ruslan Salakhutdinov, William W. Cohen

The focus of past machine learning research for Reading Comprehension tasks has been primarily on the design of novel deep learning architectures.

BIG-bench Machine Learning Reading Comprehension +1

Words or Characters? Fine-grained Gating for Reading Comprehension

1 code implementation6 Nov 2016 Zhilin Yang, Bhuwan Dhingra, Ye Yuan, Junjie Hu, William W. Cohen, Ruslan Salakhutdinov

Previous work combines word-level and character-level representations using concatenation or scalar weighting, which is suboptimal for high-level tasks like reading comprehension.

Question Answering Reading Comprehension +1

Towards End-to-End Reinforcement Learning of Dialogue Agents for Information Access

1 code implementation ACL 2017 Bhuwan Dhingra, Lihong Li, Xiujun Li, Jianfeng Gao, Yun-Nung Chen, Faisal Ahmed, Li Deng

In this paper, we address this limitation by replacing symbolic queries with an induced "soft" posterior distribution over the KB that indicates which entities the user is interested in.

reinforcement-learning Reinforcement Learning (RL) +1

Bootstrapping Distantly Supervised IE using Joint Learning and Small Well-structured Corpora

no code implementations10 Jun 2016 Lidong Bing, Bhuwan Dhingra, Kathryn Mazaitis, Jong Hyuk Park, William W. Cohen

We propose a framework to improve performance of distantly-supervised relation extraction, by jointly learning to solve two related tasks: concept-instance extraction and relation extraction.

Relation Extraction

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