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

10/10/2017
by   Seunghyun Yoon, et al.
0

In this paper, we propose a novel end-to-end neural architecture for ranking answers from candidates that adapts a hierarchical recurrent neural network and a latent topic clustering module. With our proposed model, a text is encoded to a vector representation from an word-level to a chunk-level to effectively capture the entire meaning. In particular, by adapting the hierarchical structure, our model prevents performance degradations in longer text comprehension while other recurrent neural networks suffer from it. Additionally, the latent topic clustering module extracts semantic information from target samples. This clustering module is useful for any text related tasks by allowing each data sample to find its nearest topic cluster, thus helping the neural network model analyze the entire data. We evaluate our models on the Ubuntu Dialogue Corpus and consumer electronic domain question answering dataset, which is related to Samsung products. The proposed model shows better performances than conventional architectures, resulting in state-of-the-art results for ranking question-answer pairs.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
06/01/2019

Promotion of Answer Value Measurement with Domain Effects in Community Question Answering Systems

In the area of community question answering (CQA), answer selection and ...
research
09/17/2017

Hierarchical Gated Recurrent Neural Tensor Network for Answer Triggering

In this paper, we focus on the problem of answer triggering ad-dressed b...
research
10/31/2016

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

This paper proposes dynamic chunk reader (DCR), an end-to-end neural rea...
research
12/22/2020

A Hierarchical Reasoning Graph Neural Network for The Automatic Scoring of Answer Transcriptions in Video Job Interviews

We address the task of automatically scoring the competency of candidate...
research
06/07/2019

RankQA: Neural Question Answering with Answer Re-Ranking

The conventional paradigm in neural question answering (QA) for narrativ...
research
07/12/2020

Applying recent advances in Visual Question Answering to Record Linkage

Multi-modal Record Linkage is the process of matching multi-modal record...
research
07/22/2023

Explainable Topic-Enhanced Argument Mining from Heterogeneous Sources

Given a controversial target such as “nuclear energy”, argument mining a...

Please sign up or login with your details

Forgot password? Click here to reset