Learning to Ask Conversational Questions by Optimizing Levenshtein Distance

Conversational Question Simplification (CQS) aims to simplify self-contained questions into conversational ones by incorporating some conversational characteristics, e.g., anaphora and ellipsis. Existing maximum likelihood estimation (MLE) based methods often get trapped in easily learned tokens as all tokens are treated equally during training. In this work, we introduce a Reinforcement Iterative Sequence Editing (RISE) framework that optimizes the minimum Levenshtein distance (MLD) through explicit editing actions. RISE is able to pay attention to tokens that are related to conversational characteristics. To train RISE, we devise an Iterative Reinforce Training (IRT) algorithm with a Dynamic Programming based Sampling (DPS) process to improve exploration. Experimental results on two benchmark datasets show that RISE significantly outperforms state-of-the-art methods and generalizes well on unseen data.Comment: 13 pages, 4 figures, Published in ACL 202

Question Generation Using Sequence-to-Sequence Model with Semantic Role Labels

Automatic generation of questions from text has gained increasing attention due to its useful applications. We propose a novel question generation method that combines the benefits of rule-based and neural sequence-to-sequence (Seq2Seq) models. The proposed method can automatically generate multiple questions from an input sentence covering different views of the sentence as in rule-based methods, while more complicated "rules" can be learned via the Seq2Seq model. The method utilizes semantic role labeling (SRL) used in rule-based methods to convert training examples into their semantic representations, and then trains a sequence-to-sequence model over the semantic representations. Our extensive experiments on three real-world data sets show that the proposed method significantly improves the state-of-the-art neural question generation approaches in terms of both automatic and human evaluation measures. Moreover, we extend our proposed approach to a paragraph-level SRL-based method and evaluate it on two data sets. Through both automatic and human evaluations, we show that our proposed framework remarkably improves its Seq2Seq counterparts