Effective Character-augmented Word Embedding for Machine Reading Comprehension
arXiv:1808.02772
Abstract
Machine reading comprehension is a task to model relationship between passage and query. In terms of deep learning framework, most of state-of-the-art models simply concatenate word and character level representations, which has been shown suboptimal for the concerned task. In this paper, we empirically explore different integration strategies of word and character embeddings and propose a character-augmented reader which attends character-level representation to augment word embedding with a short list to improve word representations, especially for rare words. Experimental results show that the proposed approach helps the baseline model significantly outperform state-of-the-art baselines on various public benchmarks.
arXiv commentAccepted by NLPCC 2018. Early work of arXiv:1806.09103
References in corpus (12)
- Efficient Estimation of Word Representations in Vector Space
- Teaching Machines to Read and Comprehend
- Bidirectional Attention Flow for Machine Comprehension
- Character-Aware Neural Language Models
- The Goldilocks Principle: Reading Children's Books with Explicit Memory Representations
- Machine Comprehension Using Match-LSTM and Answer Pointer
- Character-based Neural Machine Translation
- Modeling Multi-turn Conversation with Deep Utterance Aggregation
- Deep Enhanced Representation for Implicit Discourse Relation Recognition
- Lingke: A Fine-grained Multi-turn Chatbot for Customer Service
- One-shot Learning for Question-Answering in Gaokao History Challenge
- Reasoning with Memory Augmented Neural Networks for Language Comprehension