Answer Selection

15 papers with code · Natural Language Processing
Subtask of Question Answering

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The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systems

WS 2015 facebookresearch/ParlAI

This paper introduces the Ubuntu Dialogue Corpus, a dataset containing almost 1 million multi-turn dialogues, with a total of over 7 million utterances and 100 million words.

ANSWER SELECTION

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

12 Nov 2015deepmipt/DeepPavlov

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

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Neural Variational Inference for Text Processing

19 Nov 2015carpedm20/variational-text-tensorflow

We validate this framework on two very different text modelling applications, generative document modelling and supervised question answering.

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A Compare-Aggregate Model for Matching Text Sequences

6 Nov 2016shuohangwang/SeqMatchSeq

We particularly focus on the different comparison functions we can use to match two vectors.

ANSWER SELECTION READING COMPREHENSION

Simple and Effective Text Matching with Richer Alignment Features

ACL 2019 hitvoice/RE2

In this paper, we present a fast and strong neural approach for general purpose text matching applications.

ANSWER SELECTION NATURAL LANGUAGE INFERENCE PARAPHRASE IDENTIFICATION TEXT MATCHING

Hierarchical Memory Networks for Answer Selection on Unknown Words

COLING 2016 jacoxu/HMN4QA

Recently, end-to-end memory networks have shown promising results on Question Answering task, which encode the past facts into an explicit memory and perform reasoning ability by making multiple computational steps on the memory.

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

TACL 2016 Leputa/CIKM-AnalytiCup-2018

(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 PARAPHRASE IDENTIFICATION

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

7 Aug 2015codekansas/insurance_qa_python

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

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