Discourse Self-Attention for Discourse Element Identification in Argumentative Student Essays (2020.emnlp-main)
Copied to clipboard
| Challenge: | Despite its importance, discourse element identification is challenging due to the ambiguity of sentences . the number of elaboration sentences could be 10 times more than the number edna sentences. |
| Approach: | They propose to use sentence positional encodings to explicitly represent sentence positions and inter-sentence attentions to capture sentence interactions and enhance sentence representation. |
| Outcome: | The proposed model improves on a Chinese and English dataset. |
Similar Papers
Discourse-Aware Semantic Self-Attention for Narrative Reading Comprehension (D19-1)
Copied to clipboard
| Challenge: | Existing work on discourse-aware self-attention models for reading comprehension uses annotations . |
| Approach: | They propose to use linguistic annotations as a basis for a Discourse-Aware Semantic Self-Attention encoder for reading comprehension on narrative texts. |
| Outcome: | The proposed model improves reading comprehension performance on narrative texts up to +3.4 Rouge-L . it also improves inter- and cross-sentential discourse relations, sentence-internal semantic role relations, and long-distance coreference relations. |
Discourse Representation Parsing for Sentences and Documents (P19-1)
Copied to clipboard
| Challenge: | Experimental results show that our model outperforms competitive baselines by a wide margin. |
| Approach: | They propose a neural model which parses discourse structures of arbitrary length and granularity. |
| Outcome: | The proposed model outperforms baseline models on sentence- and document-level benchmarks. |
Evaluating Discourse in Structured Text Representations (P19-1)
Copied to clipboard
| Challenge: | Discourse structure is integral to understanding a text and is useful in many NLP tasks. |
| Approach: | They propose a structured attention mechanism for text classification that derives a tree over a text, akin to an RST discourse tree. |
| Outcome: | The proposed model improves performance on multiple discourse-relevant tasks and datasets and ablation studies show it does little to capture discourse structure. |
Discourse Structure Extraction from Pre-Trained and Fine-Tuned Language Models in Dialogues (2023.findings-eacl)
Copied to clipboard
| Challenge: | Discourse processing suffers from data sparsity, especially for dialogues . a variety of discourse frameworks have been proposed to extract discourse information from dialogues. |
| Approach: | They propose unsupervised and semi-supervised methods to infer latent discourse structures for dialogues based on attention matrices from Pre-trained Language Models. |
| Outcome: | The proposed methods achieve encouraging results on the STAC corpus, with F1 scores of 57.2 and 59.3 for the unsupervised and semi-supervised methods, respectively. |
A Structure-Aware Argument Encoder for Literature Discourse Analysis (2022.coling-1)
Copied to clipboard
Yinzi Li, Wei Chen, Zhongyu Wei, Yujun Huang, Chujun Wang, Siyuan Wang, Qi Zhang, Xuanjing Huang, Libo Wu
| Challenge: | Existing research for argument representation learning treats tokens in sentences equally and ignores the implied structure information of argumentative context. |
| Approach: | They propose to separate tokens into two groups to capture structural information of arguments and to incorporate paragraph-level position information into the model. |
| Outcome: | The proposed model captures structural information of arguments and is able to identify arguments automatically. |
Learning Outside the Box: Discourse-level Features Improve Metaphor Identification (N19-1)
Copied to clipboard
| Challenge: | Current approaches to metaphor identification use restricted linguistic contexts, e.g. by only considering a verb’s arguments or the sentence containing a phrase. |
| Approach: | They propose to train simple gradient boosting classifiers on representations of an utterance and its surrounding discourse learned with a variety of document embedding methods. |
| Outcome: | The proposed classifiers obtained state-of-the-art results on the 2018 VU Amsterdam metaphor identification task without complex metaphor-specific features or deep neural architectures employed by other systems. |
Interactively-Propagative Attention Learning for Implicit Discourse Relation Recognition (2020.coling-main)
Copied to clipboard
| Challenge: | Existing models for discourse relation recognition use self-attention and interactive-attention mechanisms. |
| Approach: | They develop a propagative attention learning model using a cross-coupled two-channel network. |
| Outcome: | The proposed model improves on the baseline models on a Penn Discourse Treebank. |
Dialogue Act Classification with Context-Aware Self-Attention (N19-1)
Copied to clipboard
| Challenge: | Recent work in Dialogue Act classification has treated the task as a sequence labeling problem using hierarchical deep neural networks. |
| Approach: | They propose a hierarchical deep neural network to model different levels of utterance and dialogue act semantics and use contextual dependencies to improve performance. |
| Outcome: | The proposed model improves on the Switchboard Dialogue Act Corpus while maintaining high accuracy. |
Self-Attention with Structural Position Representations (D19-1)
Copied to clipboard
| Challenge: | Experimental results show that SANs can't encode positions of input words . SAN's are currently lacking in encoding positions of words based on position-unaware "bagof-words" theory . |
| Approach: | They propose to augment SANs with structural position representations to capture latent structure of input sentence. |
| Outcome: | The proposed approach consistently outperforms the sequential representations on translation tasks. |
Exploring Discourse Structures for Argument Impact Classification (2021.acl-long)
Copied to clipboard
| Challenge: | Existing studies have shown that discourse structures influence the persuasiveness of arguments. |
| Approach: | They propose to fuse sentence-level structural discourse information with contextualized features derived from large-scale language models to investigate how discourse relations influence argument impact. |
| Outcome: | The proposed model improves its backbone RoBERTa around 1.67%, compared with other models, but side effects are brought by other models. |