The rapid development of aspect-based sentiment analysis (ABSA) within recent decades shows great potential for real-world society. The current ABSA works, however, are mostly limited to the scenario of a single text piece, leaving the study in dialogue contexts unexplored. To bridge the gap between fine-grained sentiment analysis and conversational opinion mining, in this work, we introduce a novel task of conversational aspect-based sentiment quadruple analysis, namely DiaASQ, aiming to detect the quadruple of target-aspect-opinion-sentiment in a dialogue. We manually construct a large-scale high-quality DiaASQ dataset in both Chinese and English languages. We deliberately develop a neural model to benchmark the task, which advances in effectively performing end-to-end quadruple prediction, and manages to incorporate rich dialogue-specific and discourse feature representations for better cross-utterance quadruple extraction. We hope the new benchmark will spur more advancements in the sentiment analysis community.

“基于信息论的言语产出研究发现携带信息量越大的语言单位,其语音信号越容易被强化。目前的相关研究主要通过自信息的方式衡量语言单位信息量,但该方法难以对长距离的上下文语境进行建模。本研究引入基于预训练语言模型GPT-2和文本-拼音互信息的语言单位信息量衡量方式,考察汉语的单词、韵母和声调信息量对语音产出的韵律特征的影响。研究结果显示汉语中单词和韵母信息量更大时,其韵律特征倾向于被增强,证明了我们提出的方法是有效的。其中信息量效应在音长特征上相比音高和音强特征更显著。”

This paper describes a method of generating a reduced phoneme set for dialogue-based computer assisted language learning (CALL)systems. We designed a reduced phoneme set consisting of classified phonemes more aligned with the learners’ speech characteristics than the canonical set of a target language. This reduced phoneme set provides an inherently more appropriate model for dealing with mispronunciation by second language speakers. In this study, we used a phonetic decision tree (PDT)-based top-down sequential splitting method to generate the reduced phoneme set and then applied this method to a translation-game type English CALL system for Japanese to determine its effectiveness. Experimental results showed that the proposed method improves the performance of recognizing non-native speech.