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Found 9 papers, 4 papers with code
Multi-Task Learning for Depression Detection in Dialogs
Depression is a serious mental illness that impacts the way people communicate, especially through their emotions, and, allegedly, the way they interact with others.
Investigating non lexical markers of the language of schizophrenia in spontaneous conversations
We investigate linguistic markers associated with schizophrenia in clinical conversations by detecting predictive features among French-speaking patients.
Is writing style predictive of scientific fraud?
The problem of detecting scientific fraud using machine learning was recently introduced, with initial, positive results from a model taking into account various general indicators.
Cross-lingual and cross-domain discourse segmentation of entire documents
Discourse segmentation is a crucial step in building end-to-end discourse parsers.
Cross-lingual RST Discourse Parsing
Discourse parsing is an integral part of understanding information flow and argumentative structure in documents.
Ranked #10 on
Discourse Parsing
on RST-DT
The DISRPT 2021 Shared Task on Elementary Discourse Unit Segmentation, Connective Detection, and Relation Classification
In 2021, we organized the second iteration of a shared task dedicated to the underlying units used in discourse parsing across formalisms: the DISRPT Shared Task (Discourse Relation Parsing and Treebanking).
Multi-lingual Discourse Segmentation and Connective Identification: MELODI at Disrpt2021
Building on the most successful architecture from the 2019 similar shared task, we leverage datasets in the same or similar languages to augment training data and improve on the best systems from the previous campaign on 3 out of 4 subtasks, with a mean improvement on all 16 datasets of 0. 85%.
Predicting Political Orientation in News with Latent Discourse Structure to Improve Bias Understanding
With the growing number of information sources, the problem of media bias becomes worrying for a democratic society.
Discourse Structure Extraction from Pre-Trained and Fine-Tuned Language Models in Dialogues
As a result, we explore approaches to build discourse structures for dialogues, based on attention matrices from Pre-trained Language Models (PLMs).
