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Found 21 papers, 2 papers with code
Semantic Data Set Construction from Human Clustering and Spatial Arrangement
We demonstrate how the resultant data set can be used for fine-grained analyses and evaluation of representation learning models on the intrinsic tasks of semantic clustering and semantic similarity.
Towards Better Context-aware Lexical Semantics:Adjusting Contextualized Representations through Static Anchors
One of the most powerful features of contextualized models is their dynamic embeddings for words in context, leading to state-of-the-art representations for context-aware lexical semantics.
Measuring Context-Word Biases in Lexical Semantic Datasets
Our findings demonstrate that models are usually not being tested for word-in-context semantics in the same way as humans are in these tasks, which helps us better understand the model-human gap.
AM2iCo: Evaluating Word Meaning in Context across Low-Resource Languages with Adversarial Examples
In order to address these gaps, we present AM2iCo (Adversarial and Multilingual Meaning in Context), a wide-coverage cross-lingual and multilingual evaluation set; it aims to faithfully assess the ability of state-of-the-art (SotA) representation models to understand the identity of word meaning in cross-lingual contexts for 14 language pairs.
Manual Clustering and Spatial Arrangement of Verbs for Multilingual Evaluation and Typology Analysis
We present the first evaluation of the applicability of a spatial arrangement method (SpAM) to a typologically diverse language sample, and its potential to produce semantic evaluation resources to support multilingual NLP, with a focus on verb semantics.
Spatial Multi-Arrangement for Clustering and Multi-way Similarity Dataset Construction
We present a novel methodology for fast bottom-up creation of large-scale semantic similarity resources to support development and evaluation of NLP systems.
Investigating Cross-Lingual Alignment Methods for Contextualized Embeddings with Token-Level Evaluation
In this paper, we present a thorough investigation on methods that align pre-trained contextualized embeddings into shared cross-lingual context-aware embedding space, providing strong reference benchmarks for future context-aware crosslingual models.
Second-order contexts from lexical substitutes for few-shot learning of word representations
There is a growing awareness of the need to handle rare and unseen words in word representation modelling.
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.
