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Found 23 papers, 10 papers with code
Improving Composition of Sentence Embeddings through the Lens of Statistical Relational Learning
In this work, we show that textual relational models implicitly use compositions from baseline SRL models.
The Data Provenance Initiative: A Large Scale Audit of Dataset Licensing & Attribution in AI
The race to train language models on vast, diverse, and inconsistently documented datasets has raised pressing concerns about the legal and ethical risks for practitioners.
Generating Explanations in Medical Question-Answering by Expectation Maximization Inference over Evidence
Medical Question Answering~(medical QA) systems play an essential role in assisting healthcare workers in finding answers to their questions.
When Do Discourse Markers Affect Computational Sentence Understanding?
The capabilities and use cases of automatic natural language processing (NLP) have grown significantly over the last few years.
MindGames: Targeting Theory of Mind in Large Language Models with Dynamic Epistemic Modal Logic
Here, we leverage dynamic epistemic logic to isolate a particular component of ToM and to generate controlled problems.
Ranked #1 on
Multiple-choice
on Mindgames
Generating multiple-choice questions for medical question answering with distractors and cue-masking
Medical multiple-choice question answering (MCQA) is particularly difficult.
tasksource: A Dataset Harmonization Framework for Streamlined NLP Multi-Task Learning and Evaluation
We release a dataset annotation framework and dataset annotations for more than 500 English tasks\footnote{\url{https://github. com/sileod/tasksource}}.
Ranked #4 on
Linguistic Acceptability
on CoLA
Probing neural language models for understanding of words of estimative probability
and assess whether language models can predict whether the WEP consensual probability level is close to p. Secondly, we construct a dataset of WEP-based probabilistic reasoning, to test whether language models can reason with WEP compositions.
Ranked #1 on
Natural Language Inference
on Probability words NLI
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models.
Analysis and Prediction of NLP Models Via Task Embeddings
Task embeddings are low-dimensional representations that are trained to capture task properties.
Zero-Shot Recommendation as Language Modeling
Recommendation is the task of ranking items (e. g. movies or products) according to individual user needs.
NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation
Data augmentation is an important component in the robustness evaluation of models in natural language processing (NLP) and in enhancing the diversity of the data they are trained on.
LIIR at SemEval-2021 task 6: Detection of Persuasion Techniques In Texts and Images using CLIP features
We describe our approach for SemEval-2021 task 6 on detection of persuasion techniques in multimodal content (memes).
Visual Grounding Strategies for Text-Only Natural Language Processing
Many multimodal extensions of BERT (e. g., VideoBERT, LXMERT, VL-BERT) allow a joint modeling of texts and images that lead to state-of-the-art results on multimodal tasks such as Visual Question Answering.
DiscSense: Automated Semantic Analysis of Discourse Markers
In this work, we take another perspective: using a model trained to predict discourse markers between sentence pairs, we predict plausible markers between sentence pairs with a known semantic relation (provided by existing classification datasets).
A Pragmatics-Centered Evaluation Framework for Natural Language Understanding
New models for natural language understanding have recently made an unparalleled amount of progress, which has led some researchers to suggest that the models induce universal text representations.
Aprentissage non-supervis\'e pour l'appariement et l'\'etiquetage de cas cliniques en fran\ccais - DEFT2019 (Unsupervised learning for matching and labelling of french clincal cases - DEFT2019 )
Nous pr{\'e}sentons le syst{\`e}me utilis{\'e} par l{'}{\'e}quipe Synapse/IRIT dans la comp{\'e}tition DEFT2019 portant sur deux t{\^a}ches li{\'e}es {\`a} des cas cliniques r{\'e}dig{\'e}s en fran{\c{c}}ais : l{'}une d{'}appariement entre des cas cliniques et des discussions, l{'}autre d{'}extraction de mots-clefs.
Composition of Sentence Embeddings: Lessons from Statistical Relational Learning
In this article, we show that previous work on relation prediction between texts implicitly uses compositions from baseline SRL models.
Composition of Sentence Embeddings:Lessons from Statistical Relational Learning
In this article, we show that previous work on relation prediction between texts implicitly uses compositions from baseline SRL models.
Mining Discourse Markers for Unsupervised Sentence Representation Learning
Current state of the art systems in NLP heavily rely on manually annotated datasets, which are expensive to construct.
Ranked #1 on
Relation Classification
on Discovery
Concat\'enation de r\'eseaux de neurones pour la classification de tweets, DEFT2018 (Concatenation of neural networks for tweets classification, DEFT2018 )
Nous pr{\'e}sentons le syst{\`e}me utilis{\'e} par l{'}{\'e}quipe Melodi/Synapse D{\'e}veloppement dans la comp{\'e}tition DEFT2018 portant sur la classification de th{\'e}matique ou de sentiments de tweets en fran{\c{c}}ais.
Synapse at CAp 2017 NER challenge: Fasttext CRF
We present our system for the CAp 2017 NER challenge which is about named entity recognition on French tweets.
Changement stylistique de phrases par apprentissage faiblement supervis\'e (Textual Style Transfer using Weakly Supervised Learning)
Plusieurs t{\^a}ches en traitement du langage naturel impliquent de modifier des phrases en conservant au mieux leur sens, comme la reformulation, la compression, la simplification, chacune avec leurs propres donn{\'e}es et mod{\`e}les.
