Ramesh Nallapati

research

∙ 12/20/2022

ReCode: Robustness Evaluation of Code Generation Models

Code generation models have achieved impressive performance. However, th...

0 Shiqi Wang, et al. ∙

research

∙ 12/20/2022

CoCoMIC: Code Completion By Jointly Modeling In-file and Cross-file Context

While pre-trained language models (LM) for code have achieved great succ...

0 Yangruibo Ding, et al. ∙

research

∙ 10/26/2022

Multi-lingual Evaluation of Code Generation Models

We present MBXP, an execution-based code completion benchmark in 10+ pro...

0 Ben Athiwaratkun, et al. ∙

research

∙ 10/03/2022

ContraGen: Effective Contrastive Learning For Causal Language Model

Despite exciting progress in large-scale language generation, the expres...

0 Nihal Jain, et al. ∙

research

∙ 09/28/2022

Improving Text-to-SQL Semantic Parsing with Fine-grained Query Understanding

Most recent research on Text-to-SQL semantic parsing relies on either pa...

24 Jun Wang, et al. ∙

research

∙ 05/04/2022

Efficient Few-Shot Fine-Tuning for Opinion Summarization

Abstractive summarization models are typically pre-trained on large amou...

3 Arthur Bražinskas, et al. ∙

research

∙ 03/21/2022

DQ-BART: Efficient Sequence-to-Sequence Model via Joint Distillation and Quantization

Large-scale pre-trained sequence-to-sequence models like BART and T5 ach...

0 Zheng Li, et al. ∙

research

∙ 09/12/2021

Pairwise Supervised Contrastive Learning of Sentence Representations

Many recent successes in sentence representation learning have been achi...

0 Dejiao Zhang, et al. ∙

research

∙ 05/10/2021

Improving Factual Consistency of Abstractive Summarization via Question Answering

A commonly observed problem with the state-of-the art abstractive summar...

0 Feng Nan, et al. ∙

research

∙ 04/17/2021

Transductive Learning for Abstractive News Summarization

Pre-trained language models have recently advanced abstractive summariza...

0 Arthur Bražinskas, et al. ∙

research

∙ 03/24/2021

Supporting Clustering with Contrastive Learning

Unsupervised clustering aims at discovering the semantic categories of d...

0 Dejiao Zhang, et al. ∙

research

∙ 02/18/2021

Entity-level Factual Consistency of Abstractive Text Summarization

A key challenge for abstractive summarization is ensuring factual consis...

0 Feng Nan, et al. ∙

research

∙ 11/26/2020

Answering Ambiguous Questions through Generative Evidence Fusion and Round-Trip Prediction

In open-domain question answering, questions are highly likely to be amb...

0 Yifan Gao, et al. ∙

research

∙ 10/12/2020

End-to-End Synthetic Data Generation for Domain Adaptation of Question Answering Systems

We propose an end-to-end approach for synthetic QA data generation. Our ...

0 Siamak Shakeri, et al. ∙

research

∙ 10/06/2020

Beyond [CLS] through Ranking by Generation

Generative models for Information Retrieval, where ranking of documents ...

0 Cicero Nogueira dos Santos, et al. ∙

research

∙ 09/22/2020

Embedding-based Zero-shot Retrieval through Query Generation

Passage retrieval addresses the problem of locating relevant passages, u...

0 Davis Liang, et al. ∙

research

∙ 04/24/2020

Template-Based Question Generation from Retrieved Sentences for Improved Unsupervised Question Answering

Question Answering (QA) is in increasing demand as the amount of informa...

0 Alexander R. Fabbri, et al. ∙

research

∙ 11/25/2019

Who did They Respond to? Conversation Structure Modeling using Masked Hierarchical Transformer

Conversation structure is useful for both understanding the nature of co...

0 Henghui Zhu, et al. ∙

research

∙ 10/17/2019

Universal Text Representation from BERT: An Empirical Study

We present a systematic investigation of layer-wise BERT activations for...

0 Xiaofei Ma, et al. ∙

research

∙ 09/17/2019

Multi Sense Embeddings from Topic Models

Distributed word embeddings have yielded state-of-the-art performance in...

0 Shobhit Jain, et al. ∙

research

∙ 08/22/2019

Multi-passage BERT: A Globally Normalized BERT Model for Open-domain Question Answering

BERT model has been successfully applied to open-domain QA tasks. Howeve...

11 Zhiguo Wang, et al. ∙

research

∙ 07/24/2019

Topic Modeling with Wasserstein Autoencoders

We propose a novel neural topic model in the Wasserstein autoencoders (W...

0 Feng Nan, et al. ∙

research

∙ 05/15/2019

Passage Ranking with Weak Supervision

In this paper, we propose a weak supervision framework for neural rankin...

0 Peng Xu, et al. ∙

research

∙ 05/15/2019

Passage Ranking with Weak Supervsion

In this paper, we propose a weak supervision framework for neural rankin...

0 Peng Xu, et al. ∙

research

∙ 03/20/2019

OCGAN: One-class Novelty Detection Using GANs with Constrained Latent Representations

We present a novel model called OCGAN for the classical problem of one-c...

22 Pramuditha Perera, et al. ∙

research

∙ 09/07/2018

Coherence-Aware Neural Topic Modeling

Topic models are evaluated based on their ability to describe documents ...

0 Ran Ding, et al. ∙

research

∙ 08/01/2017

SenGen: Sentence Generating Neural Variational Topic Model

We present a new topic model that generates documents by sampling a topi...

0 Ramesh Nallapati, et al. ∙

research

∙ 11/14/2016

Classify or Select: Neural Architectures for Extractive Document Summarization

We present two novel and contrasting Recurrent Neural Network (RNN) base...

0 Ramesh Nallapati, et al. ∙

research

∙ 11/14/2016

SummaRuNNer: A Recurrent Neural Network based Sequence Model for Extractive Summarization of Documents

We present SummaRuNNer, a Recurrent Neural Network (RNN) based sequence ...

0 Ramesh Nallapati, et al. ∙

research

∙ 03/26/2016

Pointing the Unknown Words

The problem of rare and unknown words is an important issue that can pot...

0 Caglar Gulcehre, et al. ∙

research

∙ 02/19/2016

Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond

In this work, we model abstractive text summarization using Attentional ...

0 Ramesh Nallapati, et al. ∙

Ramesh Nallapati

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