Neural Network Models for Implicit Discourse Relation Classification in English and Chinese without Surface Features
Inferring implicit discourse relations in natural language text is the most difficult subtask in discourse parsing. Surface features achieve good performance, but they are not readily applicable to other languages without semantic lexicons. Previous neural models require parses, surface features, or a small label set to work well. Here, we propose neural network models that are based on feedforward and long-short term memory architecture without any surface features. To our surprise, our best configured feedforward architecture outperforms LSTM-based model in most cases despite thorough tuning. Under various fine-grained label sets and a cross-linguistic setting, our feedforward models perform consistently better or at least just as well as systems that require hand-crafted surface features. Our models present the first neural Chinese discourse parser in the style of Chinese Discourse Treebank, showing that our results hold cross-linguistically.
Code (0)
등록된 구현이 없습니다.
Tasks
Discourse ParsingGeneral ClassificationImplicit Discourse Relation ClassificationRelationRelation ClassificationSimilar Papers 제목 키워드 기반
Zero-shot transfer for implicit discourse relation classification
Automatically classifying the relation between sentences in a discourse is a challenging task, in particular when there is no overt expression of the relation. It becomes even more challenging by the fact that annotated …
ClassificationGeneral ClassificationImplicit Discourse Relation ClassificationRelation+2Zero-shot transfer for implicit discourse relation classification
Automatically classifying the relation between sentences in a discourse is a challenging task, in particular when there is no overt expression of the relation. It becomes even more challenging by the fact that annotated …
ClassificationGeneral ClassificationImplicit Discourse Relation ClassificationRelation+2Semantic Graph Convolutional Network for Implicit Discourse Relation Classification
Implicit discourse relation classification is of great importance for discourse parsing, but remains a challenging problem due to the absence of explicit discourse connectives communicating these relations. Modeling the …
ClassificationDiscourse ParsingGeneral ClassificationImplicit Discourse Relation Classification+2Translating Implicit Discourse Connectives Based on Cross-lingual Annotation and Alignment
Implicit discourse connectives and relations are distributed more widely in Chinese texts, when translating into English, such connectives are usually translated explicitly. Towards Chinese-English MT, in this paper we d…
Implicit RelationsMachine TranslationTranslationTopic Tensor Network for Implicit Discourse Relation Recognition in Chinese
In the literature, most of the previous studies on English implicit discourse relation recognition only use sentence-level representations, which cannot provide enough semantic information in Chinese due to its unique pa…
RelationSentenceTensor Networks