Comparing Word Representations for Implicit Discourse Relation Classification
Summary
Implicit discourse relations lack an explicit connective such as "because" and account for roughly half of the relations in the Penn Discourse Treebank. They are hard to classify, and earlier systems relied on parsers, lexicons and heavy feature engineering. Braud and Denis ask how far cheap lexical information can go if words are represented well. They build a controlled comparison along three dimensions:
- Word representations: sparse one-hot vectors, Brown clusters, and three dense embeddings (Collobert and Weston, hierarchical log-bilinear, and Hellinger PCA).
- Words used: only the head words of the two segments, or all their tokens.
- Combination: concatenation, or a Kronecker (outer) product of the vectors.
Separate binary classifiers are trained for the top-level PDTB relations. Denser representations consistently beat sparser ones and reach or exceed state-of-the-art scores without hand-crafted features. Adding traditional features yields only small further gains, which suggests the embeddings already encode most of the useful syntactic and semantic information. Because the representations can be learned from raw text alone, the approach transfers readily to languages with few resources.
Key points
- Representations compared: one-hot word pairs, Brown clusters, Collobert and Weston, HLBL and H-PCA embeddings, all publicly available for English.
- Brown clusters questioned: a reproduction of Rutherford and Xue (2014) performs about the same as raw one-hot word pairs with extra features. Their reported gains seem to come from a tuned filtering threshold and coreference features, not from the clusters themselves.
- Best system with added features: it sets new top F1 scores for Temporal (29.30, previously 28.69) and Contingency (55.76, previously 54.42) on the PDTB.
- Gaps that remain: the best system stays below earlier work on Comparison (36.36 vs 39.70) and Expansion. Ji and Eisenstein (2014) report 79.91 on Expansion.
- Head words: the authors find head words distinctly relevant. This contradicts Park and Cardie (2012), who concluded that word-pair features add little value.
Cited by
- Deep Enhanced Representation for Implicit Discourse Relation Recognition
- Cross-lingual RST Discourse Parsing
- Implicit Discourse Relation Detection via a Deep Architecture with Gated Relevance Network
- A Regularization Approach for Incorporating Event Knowledge and Coreference Relations into Neural Discourse Parsing
- Implicit Discourse Relation Recognition using Neural Tensor Network with Interactive Attention and Sparse Learning
- TransS-Driven Joint Learning Architecture for Implicit Discourse Relation Recognition
- Implicit Discourse Relation Classification: We Need to Talk about Evaluation
- Contextualized Embeddings for Connective Disambiguation in Shallow Discourse Parsing
- Multi-task Attention-based Neural Networks for Implicit Discourse Relationship Representation and Identification
- Discourse Relation Sense Classification Using Cross-argument Semantic Similarity Based on Word Embeddings
- Employing the Correspondence of Relations and Connectives to Identify Implicit Discourse Relations via Label Embeddings
- Implicit Discourse Relation Recognition with Context-aware Character-enhanced Embeddings
- Adversarial Connective-exploiting Networks for Implicit Discourse Relation Classification
- A Systematic Study of Neural Discourse Models for Implicit Discourse Relation
- On the Need of Cross Validation for Discourse Relation Classification
- Two End-to-end Shallow Discourse Parsers for English and Chinese in CoNLL-2016 Shared Task
Cite
@inproceedings{braud2015comparing,
title = {{Comparing Word Representations for Implicit Discourse Relation Classification}},
author = {Braud, Chloé and Denis, Pascal},
booktitle = {Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing},
year = {2015},
doi = {10.18653/v1/d15-1262},
url = {https://doi.org/10.18653/v1/d15-1262}
}References
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