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Found 52 papers, 13 papers with code
Is “My Favorite New Movie” My Favorite Movie? Probing the Understanding of Recursive Noun Phrases
We introduce the Recursive Noun Phrase Challenge (RNPC), a dataset of three textual inference tasks involving textual entailment and event plausibility comparison, precisely targeting the understanding of recursive NPs.
Controlling the Imprint of Passivization and Negation in Contextualized Representations
Contextualized word representations encode rich information about syntax and semantics, alongside specificities of each context of use.
ALL Dolphins Are Intelligent and SOME Are Friendly: Probing BERT for Nouns’ Semantic Properties and their Prototypicality
Large scale language models encode rich commonsense knowledge acquired through exposure to massive data during pre-training, but their understanding of entities and their semantic properties is unclear.
Faithful Chain-of-Thought Reasoning
Together with self-consistency decoding, we achieve new state-of-the-art few-shot performance on 7 out of the 10 datasets, showing a strong synergy between faithfulness and accuracy.
Visualizing the Obvious: A Concreteness-based Ensemble Model for Noun Property Prediction
We propose to extract these properties from images and use them in an ensemble model, in order to complement the information that is extracted from language models.
Towards Faithful Model Explanation in NLP: A Survey
In this survey, we review over 110 model explanation methods in NLP through the lens of faithfulness.
How Does Data Corruption Affect Natural Language Understanding Models? A Study on GLUE datasets
A central question in natural language understanding (NLU) research is whether high performance demonstrates the models' strong reasoning capabilities.
Is "My Favorite New Movie" My Favorite Movie? Probing the Understanding of Recursive Noun Phrases
We introduce the Recursive Noun Phrase Challenge (RNPC), a dataset of three textual inference tasks involving textual entailment and event plausibility comparison, precisely targeting the understanding of recursive NPs.
ALL Dolphins Are Intelligent and SOME Are Friendly: Probing BERT for Nouns' Semantic Properties and their Prototypicality
Large scale language models encode rich commonsense knowledge acquired through exposure to massive data during pre-training, but their understanding of entities and their semantic properties is unclear.
Scalar Adjective Identification and Multilingual Ranking
The intensity relationship that holds between scalar adjectives (e. g., nice < great < wonderful) is highly relevant for natural language inference and common-sense reasoning.
Let's Play Mono-Poly: BERT Can Reveal Words' Polysemy Level and Partitionability into Senses
Pre-trained language models (LMs) encode rich information about linguistic structure but their knowledge about lexical polysemy remains unclear.
NLI Data Sanity Check: Assessing the Effect of Data Corruption on Model Performance
We propose a new diagnostics test suite which allows to assess whether a dataset constitutes a good testbed for evaluating the models' meaning understanding capabilities.
Simple-QE: Better Automatic Quality Estimation for Text Simplification
Text simplification systems generate versions of texts that are easier to understand for a broader audience.
BERT Knows Punta Cana is not just beautiful, it's gorgeous: Ranking Scalar Adjectives with Contextualised Representations
Adjectives like pretty, beautiful and gorgeous describe positive properties of the nouns they modify but with different intensity.
MULTISEM at SemEval-2020 Task 3: Fine-tuning BERT for Lexical Meaning
Our best English models occupy the third and fourth positions in the ranking for the two subtasks.
SUM-QE: a BERT-based Summary Quality Estimation Model
We propose SUM-QE, a novel Quality Estimation model for summarization based on BERT.
SumQE: a BERT-based Summary Quality Estimation Model
We propose SumQE, a novel Quality Estimation model for summarization based on BERT.
Embedding Biomedical Ontologies by Jointly Encoding Network Structure and Textual Node Descriptors
Many existing NE methods rely only on network structure, overlooking other information associated with the nodes, e. g., text describing the nodes.
Word Usage Similarity Estimation with Sentence Representations and Automatic Substitutes
Usage similarity estimation addresses the semantic proximity of word instances in different contexts.
A Comparison of Context-sensitive Models for Lexical Substitution
Word embedding representations provide good estimates of word meaning and give state-of-the art performance in semantic tasks.
Complexity-Weighted Loss and Diverse Reranking for Sentence Simplification
Sentence simplification is the task of rewriting texts so they are easier to understand.
Ranked #4 on
Text Simplification
on Newsela
Magnitude: A Fast, Efficient Universal Vector Embedding Utility Package
Vector space embedding models like word2vec, GloVe, fastText, and ELMo are extremely popular representations in natural language processing (NLP) applications.
Learning Scalar Adjective Intensity from Paraphrases
Adjectives like {``}warm{''}, {``}hot{''}, and {``}scalding{''} all describe temperature but differ in intensity.
Automated Paraphrase Lattice Creation for HyTER Machine Translation Evaluation
We propose a variant of a well-known machine translation (MT) evaluation metric, HyTER (Dreyer and Marcu, 2012), which exploits reference translations enriched with meaning equivalent expressions.
Comparing Constraints for Taxonomic Organization
In this paper, we present a head-to-head comparison of six taxonomic organization algorithms that vary with respect to their structural and transitivity constraints, and treatment of synonymy.
Simplification Using Paraphrases and Context-Based Lexical Substitution
Lexical simplification involves identifying complex words or phrases that need to be simplified, and recommending simpler meaning-preserving substitutes that can be more easily understood.
A comparative study of word embeddings and other features for lexical complexity detection in French
Lexical complexity detection is an important step for automatic text simplification which serves to make informed lexical substitutions.
Learning Translations via Matrix Completion
Bilingual Lexicon Induction is the task of learning word translations without bilingual parallel corpora.
KnowYourNyms? A Game of Semantic Relationships
Semantic relation knowledge is crucial for natural language understanding.
Learning Antonyms with Paraphrases and a Morphology-Aware Neural Network
Recognizing and distinguishing antonyms from other types of semantic relations is an essential part of language understanding systems.
Mapping the Paraphrase Database to WordNet
WordNet has facilitated important research in natural language processing but its usefulness is somewhat limited by its relatively small lexical coverage.
Word Sense Filtering Improves Embedding-Based Lexical Substitution
The role of word sense disambiguation in lexical substitution has been questioned due to the high performance of vector space models which propose good substitutes without explicitly accounting for sense.
Lecture bilingue augment\'ee par des alignements multi-niveaux (Augmenting bilingual reading with alignment information)
Le travail qui a conduit {\`a} cette d{\'e}monstration combine des outils de traitement des langues multilingues, en particulier l{'}alignement automatique, avec des techniques de visualisation et d{'}interaction.
Datasets for Aspect-Based Sentiment Analysis in French
Aspect Based Sentiment Analysis (ABSA) is the task of mining and summarizing opinions from text about specific entities and their aspects.
Semantic Clustering of Pivot Paraphrases
Paraphrases extracted from parallel corpora by the pivot method (Bannard and Callison-Burch, 2005) constitute a valuable resource for multilingual NLP applications.
Applying cross-lingual WSD to wordnet development
The automatic development of semantic resources constitutes an important challenge in the NLP community.
Boosting the Coverage of a Semantic Lexicon by Automatically Extracted Event Nominalizations
In this article, we present a distributional analysis method for extracting nominalization relations from monolingual corpora.
