A Deep Sequential Model for Discourse Parsing on Multi-Party Dialogues
نویسندگان
چکیده
منابع مشابه
Discourse parsing for multi-party chat dialogues
In this paper we present the first ever, to the best of our knowledge, discourse parser for multi-party chat dialogues. Discourse in multi-party dialogues dramatically differs from monologues since threaded conversations are commonplace rendering prediction of the discourse structure compelling. Moreover, the fact that our data come from chats renders the use of syntactic and lexical informatio...
متن کاملRecursive Deep Models for Discourse Parsing
Text-level discourse parsing remains a challenge: most approaches employ features that fail to capture the intentional, semantic, and syntactic aspects that govern discourse coherence. In this paper, we propose a recursive model for discourse parsing that jointly models distributed representations for clauses, sentences, and entire discourses. The learned representations can to some extent lear...
متن کاملA corpus for studying addressing behavior in multi-party dialogues
This paper describes a multi-modal corpus of hand-annotated meeting dialogues that was designed for studying addressing behavior in face-to-face conversations. The corpus contains annotated dialogue acts, addressees, adjacency pairs and gaze direction. First, we describe the corpus design where we present the annotation schema, annotation tools and annotation process itself. Then, we analyze th...
متن کاملA corpus for studying addressing behaviour in multi-party dialogues
1 2 Abstract This paper describes a multi-modal corpus of hand-annotated meeting 3 dialogues that was designed for studying addressing behaviour in face-to-face con4 versations. The corpus contains annotated dialogue acts, addressees, adjacency pairs 5 and gaze direction. First, we describe the corpus design where we present the 6 meetings collection, annotation scheme and annotation tools. The...
متن کاملA Three-stage Disfluency Classifier for Multi Party Dialogues
We present work on a three-stage system to detect and classify disfluencies in multi party dialogues. The system consists of a regular expression based module and two machine learning based modules. The results are compared to other work on multi party dialogues and we show that our system outperforms previously reported ones.
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Proceedings of the AAAI Conference on Artificial Intelligence
سال: 2019
ISSN: 2374-3468,2159-5399
DOI: 10.1609/aaai.v33i01.33017007