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Found 19 papers, 2 papers with code
We Need to Consider Disagreement in Evaluation
Instead, we suggest that we need to better capture the sources of disagreement to improve today’s evaluation practice.
MilaNLP @ WASSA: Does BERT Feel Sad When You Cry?
The paper describes the MilaNLP team’s submission (Bocconi University, Milan) in the WASSA 2021 Shared Task on Empathy Detection and Emotion Classification.
Hard and Soft Evaluation of NLP models with BOOtSTrap SAmpling - BooStSa
Natural Language Processing (NLP) ‘s applied nature makes it necessary to select the most effective and robust models.
SemEval-2023 Task 11: Learning With Disagreements (LeWiDi)
We report on the second LeWiDi shared task, which differs from the first edition in three crucial respects: (i) it focuses entirely on NLP, instead of both NLP and computer vision tasks in its first edition; (ii) it focuses on subjective tasks, instead of covering different types of disagreements-as training with aggregated labels for subjective NLP tasks is a particularly obvious misrepresentation of the data; and (iii) for the evaluation, we concentrate on soft approaches to evaluation.
Leveraging Social Interactions to Detect Misinformation on Social Media
Keeping track of the sequence of the interactions during the time, we improve over previous state-of-the-art models.
ProSiT! Latent Variable Discovery with PROgressive SImilarity Thresholds
The most common ways to explore latent document dimensions are topic models and clustering methods.
SemEval-2021 Task 12: Learning with Disagreements
Disagreement between coders is ubiquitous in virtually all datasets annotated with human judgements in both natural language processing and computer vision.
Beyond Black \& White: Leveraging Annotator Disagreement via Soft-Label Multi-Task Learning
Supervised learning assumes that a ground truth label exists.
BERTective: Language Models and Contextual Information for Deception Detection
In most cases, however, the target texts{'} preceding context is not considered.
Helpful or Hierarchical? Predicting the Communicative Strategies of Chat Participants, and their Impact on Success
When interacting with each other, we motivate, advise, inform, show love or power towards our peers.
``You Sound Just Like Your Father'' Commercial Machine Translation Systems Include Stylistic Biases
The main goal of machine translation has been to convey the correct content.
Dense Node Representation for Geolocation
Prior research has shown that geolocation can be substantially improved by including user network information.
Identifying Linguistic Areas for Geolocation
We create three sets of labels at different levels of granularity, and compare performance of a state-of-the-art geolocation model trained and tested with P2C labels to one with regular k-d tree labels.
Geolocation with Attention-Based Multitask Learning Models
Geolocation, predicting the location of a post based on text and other information, has a huge potential for several social media applications.
Increasing In-Class Similarity by Retrofitting Embeddings with Demographic Information
We use homophily cues to retrofit text-based author representations with non-linguistic information, and introduce a trade-off parameter.
DeCour: a corpus of DEceptive statements in Italian COURts
In criminal proceedings, sometimes it is not easy to evaluate the sincerity of oral testimonies.
