Answer Selection

48 papers with code • 6 benchmarks • 10 datasets

Answer Selection is the task of identifying the correct answer to a question from a pool of candidate answers. This task can be formulated as a classification or a ranking problem.

Source: Learning Analogy-Preserving Sentence Embeddings for Answer Selection

Most implemented papers

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

npow/ubottu WS 2015

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.

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

yinwenpeng/Answer_Selection TACL 2016

(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.

Neural Variational Inference for Text Processing

carpedm20/variational-text-tensorflow 19 Nov 2015

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

Gated-Attention Readers for Text Comprehension

bdhingra/ga-reader ACL 2017

In this paper we study the problem of answering cloze-style questions over documents.

Attentive Pooling Networks

winter1997/Attentive-Pooling-Networks 11 Feb 2016

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

Learning to Rank Question-Answer Pairs using Hierarchical Recurrent Encoder with Latent Topic Clustering

david-yoon/QA_HRDE_LTC NAACL 2018

In this paper, we propose a novel end-to-end neural architecture for ranking candidate answers, that adapts a hierarchical recurrent neural network and a latent topic clustering module.

Simple and Effective Text Matching with Richer Alignment Features

hitvoice/RE2 ACL 2019

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

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

shuzi/insuranceQA 7 Aug 2015

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

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

deepmipt/DeepPavlov 12 Nov 2015

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

A Compare-Aggregate Model for Matching Text Sequences

shuohangwang/SeqMatchSeq 6 Nov 2016

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