| Challenge: | Dependency parsing of conversational input can help to understand dialogs . currently available annotation schemes do not adapt well to spoken human-machine dialogs. |
| Approach: | They propose an annotation scheme that extends Universal Dependencies guidelines to spoken dialogs. |
| Outcome: | The proposed scheme disambiguates relationships between entities extracted from dialogs . it is better than existing models on public datasets and fine-tuned on ConvBank data . |
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| Challenge: | Discourse dependency parsing is a task that requires a large amount of training data, but there is little research on it. |
| Approach: | They propose to adapt unsupervised syntactic dependency parsing methods for unsupervised discourse dependency parses using unlabeled training data. |
| Outcome: | The proposed methods outperform existing methods in semi-supervised and supervised settings and outperformed existing methods. |
Enhancing Discourse Dependency Parsing with Sentence Dependency Parsing: A Unified Generative Method Based on Code Representation (2024.findings-emnlp)
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| Challenge: | Existing annotation resources for Discourse Dependency Parsing tasks are limited due to their complexity and annotation schema differences. |
| Approach: | They propose a code-based unified dependency parsing method that uses code to model dependency parses under different annotation schemas. |
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MIDAS: A Dialog Act Annotation Scheme for Open Domain HumanMachine Spoken Conversations (2021.eacl-main)
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| Challenge: | Existing dialog act schemes are designed for human-human conversations, but are not suitable for automatic speech recognition. |
| Approach: | They propose a dialog act annotation scheme for open-domain human-machine conversations . they collected 24K utterances from a large open- domain spoken conversation dataset . |
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Out-of-Domain Discourse Dependency Parsing via Bootstrapping: An Empirical Analysis on Its Effectiveness and Limitation (2022.tacl-1)
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| Challenge: | Discourse parsing accuracy degrades significantly on out-of-domain text. |
| Approach: | They propose to use bootstrapping methods to adapt modern discourse dependency parsers to out-of-domain text without additional human supervision. |
| Outcome: | The proposed methods are significantly and consistently effective for unsupervised domain adaptation of discourse dependency parsing, but the low coverage of accurately predicted pseudo labels is a bottleneck for further improvement. |
Dialo-AP: A Dependency Parsing Based Argument Parser for Dialogues (2022.coling-1)
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| Challenge: | a recent work on argument mining has focused on parsing monologues, while neglecting dialogues. |
| Approach: | They propose an end-to-end argument parser that constructs argument graphs from dialogues . they use extensive pre-training and curriculum learning to train AM . |
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Yet Another Format of Universal Dependencies for Korean (2022.coling-1)
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Yige Chen, Eunkyul Leah Jo, Yundong Yao, KyungTae Lim, Miikka Silfverberg, Francis M. Tyers, Jungyeul Park
| Challenge: | Existing dependency parsers for Korean do not perform as well as their English counterparts due to the complexity of Korean's linguistic features. |
| Approach: | They propose a morpheme-based Korean dependency parsing format and propose to adopt it to Universal Dependencies. |
| Outcome: | The proposed format outperforms parsing results for Korean UD treebanks and detailed error analysis. |
A Survey of Unsupervised Dependency Parsing (2020.coling-main)
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| Challenge: | Syntactic dependency parsing is an important task in natural language processing . unsupervised learning of dependency parses requires training sentences to be manually annotated with their correct parse trees. |
| Approach: | They propose to survey existing approaches to unsupervised dependency parsing . they identify two major classes of approaches and discuss recent trends . |
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Spoken Language Treebanks in Universal Dependencies: an Overview (2022.lrec-1)
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| Challenge: | spoken language treebanks have divergent annotation schemes limiting cross-resource explorations . many spoken language trees have no written form, but many of the world languages have no spoken form at all. |
| Approach: | They propose to use the Universal Dependencies annotation scheme to annotate spoken language treebanks using a morphosyntactic annotation scheme. |
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Dialogue-Based Relation Extraction (2020.acl-main)
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| Challenge: | Existing dialogue-based relation extraction tasks focus on texts from formal genres such as professionally written and edited news reports or well-edited websites. |
| Approach: | They propose to use DialogRE to study cross-sentence relation extraction . they propose to annotate 36 possible relation types between arguments in dialogues . |
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Language Model as an Annotator: Exploring DialoGPT for Dialogue Summarization (2021.acl-long)
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| Challenge: | Existing dialogue summarization systems encode text with a number of general semantic features, but these are often not available in open-domain tools. |
| Approach: | They propose to use DialoGPT to label three types of features on two datasets . they propose to employ pre-trained and non-pre-tried models as dialogue annotators . |
| Outcome: | The proposed method improves on two dialogue summarization datasets and achieves state-of-the-art performance. |