Distributed Marker Representation for Ambiguous Discourse Markers and Entangled Relations (2023.acl-long)
Copied to clipboard
| Challenge: | Discourse markers are natural representations of discourse in our daily language. |
| Approach: | They propose to use unlimited discourse marker data to learn a Distributed Marker Representation by bridging markers with sentence pairs. |
| Outcome: | The proposed model outperforms existing models on the implicit discourse relation recognition task and provides strong interpretability. |
Similar Papers
Mining Discourse Markers for Unsupervised Sentence Representation Learning (N19-1)
Copied to clipboard
| Challenge: | Current state of the art systems in NLP heavily rely on manually annotated datasets, which are expensive to obtain and are ineffective to extract. |
| Approach: | They propose to automatically discover sentence pairs with relevant discourse markers and apply it to massive amounts of data. |
| Outcome: | The proposed method can learn transferable sentence embeddings from 174 discourse markers even for rare markers such as “coincidentally” or “amazingly”. |
ISO-based Annotated Multilingual Parallel Corpus for Discourse Markers (2022.lrec-1)
Copied to clipboard
Purificação Silvano, Mariana Damova, Giedrė Valūnaitė Oleškevičienė, Chaya Liebeskind, Christian Chiarcos, Dimitar Trajanov, Ciprian-Octavian Truică, Elena-Simona Apostol, Anna Baczkowska
| Challenge: | Discourse markers carry information about the discourse structure and organization, and also signal local dependencies or epistemic stance of speaker. |
| Approach: | They propose an ISO-based annotated multilingual parallel corpus for discourse markers . they propose an annotation scheme for discourse relations with a plug-in to ISO 24617-2 . |
| Outcome: | The proposed language resource is based on an ISO-based annotated multilingual parallel corpus of discourse markers. |
DiscSense: Automated Semantic Analysis of Discourse Markers (2020.lrec-1)
Copied to clipboard
| Challenge: | Existing models for predicting discourse markers have been used to study link between markers and semantic relations . |
| Approach: | They use a model trained to predict discourse markers between sentence pairs to predict plausible markers between sentences with a known semantic relation. |
| Outcome: | The proposed method predicts markers between sentence pairs with a known semantic relation . the resulting dataset, named DiscSense, is publicly available . |
Inducing Discourse Marker Inventories from Lexical Knowledge Graphs (2022.lrec-1)
Copied to clipboard
| Challenge: | Discourse marker inventories are important tools for the development of discourse parsers and corpora with discourse annotations. |
| Approach: | They explore the potential of multilingual lexical knowledge graphs to induce multilingual discourse marker lexicons using concept propagation methods previously developed in translation inference across dictionaries. |
| Outcome: | The proposed method can induce multilingual discourse marker lexicons using multilingual knowledge graphs. |
DisSent: Learning Sentence Representations from Explicit Discourse Relations (P19-1)
Copied to clipboard
| Challenge: | Existing models train on vast amounts of text or require costly, manually curated datasets. |
| Approach: | They propose to leverage the discourse relations between sentences to curate a high quality sentence relation task by leveraging explicit discourse relations. |
| Outcome: | The proposed model can be used to learn the meaning of two sentences in a bidirectional LSTM sentence encoder. |
A Lexicon of Discourse Markers for Portuguese – LDM-PT (L18-1)
Copied to clipboard
| Challenge: | lexicon of discourse markers for European Portuguese is composed of 252 pairs of discourse marker/rhetorical sense . lexical items have the function of structuring discourse and ensuring textual cohesion and coherence at intra-sentential and inter-sententential levels. |
| Approach: | They propose to create a lexicon of Portuguese discourse markers that contains 252 pairs of discourse markers/rhetorical sense. |
| Outcome: | The lexicon is compiled in an excel spread sheet and converted to an XML scheme compatible with the DiMLex format. |
Threads of Subtlety: Detecting Machine-Generated Texts Through Discourse Motifs (2024.acl-long)
Copied to clipboard
| Challenge: | Empirical findings show that although both LLMs and humans generate distinct discourse patterns influenced by specific domains, human-written texts exhibit more structural variability, reflecting the nuanced nature of human writing in different domains. |
| Approach: | They propose a method to leverage hierarchical parse trees and recursive hypergraphs to uncover distinctive discourse patterns in texts written by humans and LLMs. |
| Outcome: | The proposed method combines hierarchical parse trees and recursive hypergraphs to uncover distinctive discourse patterns in texts produced by both LLMs and humans. |
Pre-training Multi-party Dialogue Models with Latent Discourse Inference (2023.acl-long)
Copied to clipboard
| Challenge: | Existing studies have failed to scale up the pre-training process by putting aside unlabeled data . et al., 2019: multi-party dialogues are more difficult for models to understand since they involve multiple interlocutors resulting in interweaving reply-to relations and information flows. |
| Approach: | They propose to treat discourse structures as latent variables and jointly infer them to pre-train a model that understands the discourse structure of multi-party dialogues. |
| Outcome: | The proposed model outperforms baselines and achieves state-of-the-art results on multiple downstream tasks. |
Improving Implicit Discourse Relation Classification by Modeling Inter-dependencies of Discourse Units in a Paragraph (N18-1)
Copied to clipboard
| Challenge: | Existing methods for predicting implicit discourse relations ignore wider paragraph contexts beyond the two discourse units examined for a discourse relation prediction. |
| Approach: | They propose a paragraph-level neural network that models inter-dependencies between discourse units and discourse relation continuity and patterns and predicts a sequence of discourse relations in a sentence. |
| Outcome: | The proposed model outperforms state-of-the-art systems on the benchmark corpus of PDTB. |
Discourse Marker Augmented Network with Reinforcement Learning for Natural Language Inference (P18-1)
Copied to clipboard
| Challenge: | Existing approaches to natural language inference focus on interaction architectures of sentences . but, we propose to transfer knowledge from discourse markers to augment the model . |
| Approach: | They propose to transfer knowledge from discourse markers to augment the quality of the NLI model. |
| Outcome: | The proposed method achieves state-of-the-art performance on large-scale datasets. |