Search Results for author: Bing Xiang

Found 56 papers, 20 papers with code

H2KGAT: Hierarchical Hyperbolic Knowledge Graph Attention Network

no code implementations EMNLP 2020 Shen Wang, Xiaokai Wei, Cicero Nogueira dos santos, Zhiguo Wang, Ramesh Nallapati, Andrew Arnold, Bing Xiang, Philip S. Yu

Existing knowledge graph embedding approaches concentrate on modeling symmetry/asymmetry, inversion, and composition typed relations but overlook the hierarchical nature of relations.

Graph Attention Knowledge Graph Embedding +2

Joint Text and Label Generation for Spoken Language Understanding

no code implementations11 May 2021 Yang Li, Ben Athiwaratkun, Cicero Nogueira dos santos, Bing Xiang

In this work, we propose to leverage the prior information embedded in pretrained language models (LM) to improve generalization for intent classification and slot labeling tasks with limited training data.

Intent Classification Learning with noisy labels +1

Improving Factual Consistency of Abstractive Summarization via Question Answering

1 code implementation10 May 2021 Feng Nan, Cicero Nogueira dos santos, Henghui Zhu, Patrick Ng, Kathleen McKeown, Ramesh Nallapati, Dejiao Zhang, Zhiguo Wang, Andrew O. Arnold, Bing Xiang

A commonly observed problem with the state-of-the art abstractive summarization models is that the generated summaries can be factually inconsistent with the input documents.

Abstractive Text Summarization Question Answering

Generative Context Pair Selection for Multi-hop Question Answering

no code implementations18 Apr 2021 Dheeru Dua, Cicero Nogueira dos santos, Patrick Ng, Ben Athiwaratkun, Bing Xiang, Matt Gardner, Sameer Singh

Compositional reasoning tasks like multi-hop question answering, require making latent decisions to get the final answer, given a question.

Multi-hop Question Answering Question Answering

Retrieval, Re-ranking and Multi-task Learning for Knowledge-Base Question Answering

no code implementations EACL 2021 Zhiguo Wang, Patrick Ng, Ramesh Nallapati, Bing Xiang

Experiments show that: (1) Our IR-based retrieval method is able to collect high-quality candidates efficiently, thus enables our method adapt to large-scale KBs easily; (2) the BERT model improves the accuracy across all three sub-tasks; and (3) benefiting from multi-task learning, the unified model obtains further improvements with only 1/3 of the original parameters.

Entity Linking Information Retrieval +3

Structured Prediction as Translation between Augmented Natural Languages

no code implementations ICLR 2021 Giovanni Paolini, Ben Athiwaratkun, Jason Krone, Jie Ma, Alessandro Achille, Rishita Anubhai, Cicero Nogueira dos santos, Bing Xiang, Stefano Soatto

We propose a new framework, Translation between Augmented Natural Languages (TANL), to solve many structured prediction language tasks including joint entity and relation extraction, nested named entity recognition, relation classification, semantic role labeling, event extraction, coreference resolution, and dialogue state tracking.

Coreference Resolution Dialogue State Tracking +7

Learning Contextual Representations for Semantic Parsing with Generation-Augmented Pre-Training

no code implementations18 Dec 2020 Peng Shi, Patrick Ng, Zhiguo Wang, Henghui Zhu, Alexander Hanbo Li, Jun Wang, Cicero Nogueira dos santos, Bing Xiang

Most recently, there has been significant interest in learning contextual representations for various NLP tasks, by leveraging large scale text corpora to train large neural language models with self-supervised learning objectives, such as Masked Language Model (MLM).

Language Modelling Self-Supervised Learning +2

Beyond [CLS] through Ranking by Generation

no code implementations EMNLP 2020 Cicero Nogueira dos santos, Xiaofei Ma, Ramesh Nallapati, Zhiheng Huang, Bing Xiang

Generative models for Information Retrieval, where ranking of documents is viewed as the task of generating a query from a document's language model, were very successful in various IR tasks in the past.

Answer Selection Information Retrieval +3

Embedding-based Zero-shot Retrieval through Query Generation

1 code implementation22 Sep 2020 Davis Liang, Peng Xu, Siamak Shakeri, Cicero Nogueira dos Santos, Ramesh Nallapati, Zhiheng Huang, Bing Xiang

In some cases, our model trained on synthetic data can even outperform the same model trained on real data

Passage Retrieval

Augmented Natural Language for Generative Sequence Labeling

no code implementations EMNLP 2020 Ben Athiwaratkun, Cicero Nogueira dos santos, Jason Krone, Bing Xiang

We set a new state-of-the-art for few-shot slot labeling, improving substantially upon the previous 5-shot ($75. 0\% \rightarrow 90. 9\%$) and 1-shot ($70. 4\% \rightarrow 81. 0\%$) state-of-the-art results.

Intent Classification Named Entity Recognition

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

1 code implementation ACL 2020 Alexander R. Fabbri, Patrick Ng, Zhiguo Wang, Ramesh Nallapati, Bing Xiang

Training a QA model on this data gives a relative improvement over a previous unsupervised model in F1 score on the SQuAD dataset by about 14%, and 20% when the answer is a named entity, achieving state-of-the-art performance on SQuAD for unsupervised QA.

Language Modelling Question Answering +1

TRANS-BLSTM: Transformer with Bidirectional LSTM for Language Understanding

no code implementations16 Mar 2020 Zhiheng Huang, Peng Xu, Davis Liang, Ajay Mishra, Bing Xiang

Prior to the transformer era, bidirectional Long Short-Term Memory (BLSTM) has been the dominant modeling architecture for neural machine translation and question answering.

Machine Translation Question Answering +1

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

1 code implementation25 Nov 2019 Henghui Zhu, Feng Nan, Zhiguo Wang, Ramesh Nallapati, Bing Xiang

In this work, we define the problem of conversation structure modeling as identifying the parent utterance(s) to which each utterance in the conversation responds to.

Platform

Universal Text Representation from BERT: An Empirical Study

no code implementations17 Oct 2019 Xiaofei Ma, Zhiguo Wang, Patrick Ng, Ramesh Nallapati, Bing Xiang

We present a systematic investigation of layer-wise BERT activations for general-purpose text representations to understand what linguistic information they capture and how transferable they are across different tasks.

Learning-To-Rank Natural Language Inference +3

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

no code implementations IJCNLP 2019 Zhiguo Wang, Patrick Ng, Xiaofei Ma, Ramesh Nallapati, Bing Xiang

To tackle this issue, we propose a multi-passage BERT model to globally normalize answer scores across all passages of the same question, and this change enables our QA model find better answers by utilizing more passages.

Open-Domain Question Answering

Topic Modeling with Wasserstein Autoencoders

1 code implementation ACL 2019 Feng Nan, Ran Ding, Ramesh Nallapati, Bing Xiang

To measure the diversity of the produced topics, we propose a simple topic uniqueness metric.

Topic Models

Passage Ranking with Weak Supervision

no code implementations15 May 2019 Peng Xu, Xiaofei Ma, Ramesh Nallapati, Bing Xiang

In this paper, we propose a \textit{weak supervision} framework for neural ranking tasks based on the data programming paradigm \citep{Ratner2016}, which enables us to leverage multiple weak supervision signals from different sources.

WeNet: Weighted Networks for Recurrent Network Architecture Search

no code implementations8 Apr 2019 Zhiheng Huang, Bing Xiang

In this paper, we propose a novel way of architecture search by means of weighted networks (WeNet), which consist of a number of networks, with each assigned a weight.

General Classification Image Classification +2

Coherence-Aware Neural Topic Modeling

2 code implementations EMNLP 2018 Ran Ding, Ramesh Nallapati, Bing Xiang

Topic models are evaluated based on their ability to describe documents well (i. e. low perplexity) and to produce topics that carry coherent semantic meaning.

Topic Models Variational Inference

Jointly Trained Sequential Labeling and Classification by Sparse Attention Neural Networks

no code implementations28 Sep 2017 Mingbo Ma, Kai Zhao, Liang Huang, Bing Xiang, Bo-Wen Zhou

In order to utilize the potential benefits from their correlations, we propose a jointly trained model for learning the two tasks simultaneously via Long Short-Term Memory (LSTM) networks.

General Classification Intent Classification +3

Neural Models for Sequence Chunking

1 code implementation15 Jan 2017 Feifei Zhai, Saloni Potdar, Bing Xiang, Bo-Wen Zhou

Many natural language understanding (NLU) tasks, such as shallow parsing (i. e., text chunking) and semantic slot filling, require the assignment of representative labels to the meaningful chunks in a sentence.

Chunking Natural Language Understanding +1

GaDei: On Scale-up Training As A Service For Deep Learning

no code implementations18 Nov 2016 Wei Zhang, Minwei Feng, Yunhui Zheng, Yufei Ren, Yandong Wang, Ji Liu, Peng Liu, Bing Xiang, Li Zhang, Bo-Wen Zhou, Fei Wang

By evaluating the NLC workloads, we show that only the conservative hyper-parameter setup (e. g., small mini-batch size and small learning rate) can guarantee acceptable model accuracy for a wide range of customers.

End-to-End Answer Chunk Extraction and Ranking for Reading Comprehension

no code implementations31 Oct 2016 Yang Yu, Wei zhang, Kazi Hasan, Mo Yu, Bing Xiang, Bo-Wen Zhou

This paper proposes dynamic chunk reader (DCR), an end-to-end neural reading comprehension (RC) model that is able to extract and rank a set of answer candidates from a given document to answer questions.

Question Answering Reading Comprehension

Improved Representation Learning for Question Answer Matching

no code implementations1 Aug 2016 Ming Tan, Cicero dos Santos, Bing Xiang, BoWen Zhou

Passage-level question answer matching is a challenging task since it requires effective representations that capture the complex semantic relations between questions and answers.

Answer Selection Representation Learning

Simple Question Answering by Attentive Convolutional Neural Network

no code implementations COLING 2016 Wenpeng Yin, Mo Yu, Bing Xiang, Bo-Wen Zhou, Hinrich Schütze

In fact selection, we match the subject entity in a fact candidate with the entity mention in the question by a character-level convolutional neural network (char-CNN), and match the predicate in that fact with the question by a word-level CNN (word-CNN).

Entity Linking Question Answering

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

4 code implementations CONLL 2016 Ramesh Nallapati, Bo-Wen Zhou, Cicero Nogueira dos santos, Caglar Gulcehre, Bing Xiang

In this work, we model abstractive text summarization using Attentional Encoder-Decoder Recurrent Neural Networks, and show that they achieve state-of-the-art performance on two different corpora.

Abstractive Text Summarization Sentence Summarization

Attentive Pooling Networks

3 code implementations11 Feb 2016 Cicero dos Santos, Ming Tan, Bing Xiang, Bo-Wen Zhou

In this work, we propose Attentive Pooling (AP), a two-way attention mechanism for discriminative model training.

Answer Selection Representation Learning

Leveraging Sentence-level Information with Encoder LSTM for Semantic Slot Filling

no code implementations EMNLP 2016 Gakuto Kurata, Bing Xiang, Bo-Wen Zhou, Mo Yu

Recurrent Neural Network (RNN) and one of its specific architectures, Long Short-Term Memory (LSTM), have been widely used for sequence labeling.

Natural Language Understanding Slot Filling

ABCNN: Attention-Based Convolutional Neural Network for Modeling Sentence Pairs

8 code implementations TACL 2016 Wenpeng Yin, Hinrich Schütze, Bing Xiang, Bo-Wen Zhou

(ii) We propose three attention schemes that integrate mutual influence between sentences into CNN; thus, the representation of each sentence takes into consideration its counterpart.

Answer Selection Natural Language Inference +1

Good, Better, Best: Choosing Word Embedding Context

no code implementations19 Nov 2015 James Cross, Bing Xiang, Bo-Wen Zhou

We propose two methods of learning vector representations of words and phrases that each combine sentence context with structural features extracted from dependency trees.

LSTM-based Deep Learning Models for Non-factoid Answer Selection

1 code implementation12 Nov 2015 Ming Tan, Cicero dos Santos, Bing Xiang, Bo-Wen Zhou

One direction is to define a more composite representation for questions and answers by combining convolutional neural network with the basic framework.

Answer Selection

Distributed Deep Learning for Question Answering

no code implementations3 Nov 2015 Minwei Feng, Bing Xiang, Bo-Wen Zhou

This paper is an empirical study of the distributed deep learning for question answering subtasks: answer selection and question classification.

Answer Selection General Classification

Empirical Study on Deep Learning Models for Question Answering

no code implementations26 Oct 2015 Yang Yu, Wei zhang, Chung-Wei Hang, Bing Xiang, Bo-Wen Zhou

In this paper we explore deep learning models with memory component or attention mechanism for question answering task.

Machine Translation Question Answering

Applying Deep Learning to Answer Selection: A Study and An Open Task

3 code implementations7 Aug 2015 Minwei Feng, Bing Xiang, Michael R. Glass, Lidan Wang, Bo-Wen Zhou

We apply a general deep learning framework to address the non-factoid question answering task.

Answer Selection

Dependency-based Convolutional Neural Networks for Sentence Embedding

1 code implementation IJCNLP 2015 Mingbo Ma, Liang Huang, Bing Xiang, Bo-Wen Zhou

In sentence modeling and classification, convolutional neural network approaches have recently achieved state-of-the-art results, but all such efforts process word vectors sequentially and neglect long-distance dependencies.

General Classification Sentence Embedding

Classifying Relations by Ranking with Convolutional Neural Networks

2 code implementations IJCNLP 2015 Cicero Nogueira dos Santos, Bing Xiang, Bo-Wen Zhou

Relation classification is an important semantic processing task for which state-ofthe-art systems still rely on costly handcrafted features.

General Classification Relation Classification +1

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