Learning to Ask Conversational Questions by Optimizing L evenshtein Distance (2021)

Abstract: 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 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 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.

## introduction conversational information seeking (cis) @xcite has received extensive attention. it introduces a new way to connect people to information through conversations @xcite @xcite . one of the key features of cis is mixed initiative behavior, where a system can improve user satisfaction by proactively asking clarification questions @xcite @xcite , besides passively providing answers @xcite @xcite . previous studies on asking clarification questions can be grouped into two categories: conversational question generation @xcite and conversational question ranking @xcite . what was ira hayes doing after the war? hayes attempted to lead a normal civilian life after the war. ## rise to optimize mld in eq. 2, we devise the reinforcement iterative sequence editing (rise) framework, which reformulates the optimization of mld as a hierarchical combinatorial markov decision process (hcmdp). next, we first describe our hcmdp formulation of rise. we then detail the modeling of each ingredient in rise. finally, we present the training process of rise. ## analysis 5.1 ablation study to analyze where the improvements of rise come from, we conduct an ablation study on the canard and cast datasets (see ## conclusion in this paper, we have proposed a minimum levenshtein distance (mld) based reinforcement iterative sequence editing (rise) framework for conversational question simplification (cqs). to train rise, we have devised an iterative reinforce training (irt) algorithm with a novel dynamic programming based sampling (dps) process. extensive experiments show that rise is more effective and robust than several state-of-the-art cqs methods. a limitation of rise is that it may fail to decide whether to stop or continue editing and leave out necessary information. in future work, we plan to address this issue by learning a reward function that considers the whole editing process through adversarial learning @xcite .