Papers by Chloé Braud
Zero-shot Learning for Multilingual Discourse Relation Classification (2024.lrec-main)
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| Challenge: | Discourse analysis is a hard task, but data is limited for other languages. |
| Approach: | They propose to use zero-shot learning to combine discourse relation data . they compare two versions of the same text with different labels . |
| Outcome: | The proposed method can be applied to languages, frameworks, or similarity measures. |
Augmenting Transformers with KNN-Based Composite Memory for Dialog (2021.tacl-1)
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| Challenge: | Recent work has focused on learning architectures with large memories capable of storing external knowledge. |
| Approach: | They propose a method to augment generative Transformer neural networks with information fetching modules. |
| Outcome: | The proposed approach improves performance in generative dialog modeling . external knowledge is retrieved from Wikipedia, images, and human-written dialog utterances . |
Using Local Knowledge Graph Construction to Scale Seq2Seq Models to Multi-Document Inputs (D19-1)
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| Challenge: | Current approaches extract portions of web text as input to Sequence-to-Sequence models . a problem is generating relevant knowledge from noisy and redundant input such as webpages . |
| Approach: | They propose to restructure free text into local knowledge graphs that are linearized into sequences . they propose to encode the graph as a sequence and then linearize it into a structured sequence . |
| Outcome: | The proposed method compresses web search information and reduces redundancy . it can be used for open-domain question answering and multi-document summarization tasks . |
An Integrated Approach for Political Bias Prediction and Explanation Based on Discursive Structure (2023.findings-acl)
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| Challenge: | Existing methods for predicting and explaining political biases rely on lexical cues. |
| Approach: | They propose an approach to automatically characterize biases that takes into account structural differences and is efficient for long texts. |
| Outcome: | The proposed approach is efficient for long texts and takes into account structural differences. |
DISRPT: A Multilingual, Multi-domain, Cross-framework Benchmark for Discourse Processing (2024.lrec-main)
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Chloé Braud, Amir Zeldes, Laura Rivière, Yang Janet Liu, Philippe Muller, Damien Sileo, Tatsuya Aoyama
| Challenge: | DISRPT is a multilingual, multi-domain, and cross-framework benchmark dataset for discourse processing. |
| Approach: | They present a multilingual, multi-domain, and cross-framework benchmark dataset for discourse processing that includes 13 languages and 24 corpora covering about 4 millions tokens and around 250,000 discourse relation instances from 4 discourse frameworks. |
| Outcome: | The DISRPT dataset includes data from 24 corpora covering about 4 millions tokens and around 250,000 discourse relation instances from 4 discourse frameworks. |