Modeling Dialogue Acts with Content Word Filtering and Speaker Preferences
We present an unsupervised model of dialogue act sequences in conversation. By modeling topical themes as transitioning more slowly than dialogue acts in conversation, our model de-emphasizes content-related words in order to focus on conversational function words that signal dialogue acts. We also incorporate speaker tendencies to use some acts more than others as an additional predictor of dialogue act prevalence beyond temporal dependencies. According to the evaluation presented on two dissimilar corpora, the CNET forum and NPS Chat corpus, the effectiveness of each modeling assumption is found to vary depending on characteristics of the data. De-emphasizing content-related words yields improvement on the CNET corpus, while utilizing speaker tendencies is advantageous on the NPS corpus. The components of our model complement one another to achieve robust performance on both corpora and outperform state-of-the-art baseline models.
Code (0)
등록된 구현이 없습니다.
Tasks
Language ModelingLanguage ModellingSimilar Papers 제목 키워드 기반
ASR Adaptation for E-commerce Chatbots using Cross-Utterance Context and Multi-Task Language Modeling
Automatic Speech Recognition (ASR) robustness toward slot entities are critical in e-commerce voice assistants that involve monetary transactions and purchases. Along with effective domain adaptation, it is intuitive tha…
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)Domain AdaptationLanguage Modeling+3User Satisfaction Estimation with Sequential Dialogue Act Modeling in Goal-oriented Conversational Systems
User Satisfaction Estimation (USE) is an important yet challenging task in goal-oriented conversational systems. Whether the user is satisfied with the system largely depends on the fulfillment of the user's needs, which…
Modeling Long-Range Context for Concurrent Dialogue Acts Recognition
In dialogues, an utterance is a chain of consecutive sentences produced by one speaker which ranges from a short sentence to a thousand-word post. When studying dialogues at the utterance level, it is not uncommon that a…
SentenceDialogue History Matters! Personalized Response Selectionin Multi-turn Retrieval-based Chatbots
Existing multi-turn context-response matching methods mainly concentrate on obtaining multi-level and multi-dimension representations and better interactions between context utterances and response. However, in real-plac…
Representation LearningRetrievalUser IdentificationAbstractive Dialogue Summarization with Sentence-Gated Modeling Optimized by Dialogue Acts
Neural abstractive summarization has been increasingly studied, where the prior work mainly focused on summarizing single-speaker documents (news, scientific publications, etc). In dialogues, there are different interact…
Abstractive Dialogue SummarizationAbstractive Text SummarizationSentence