Search Results for author: Diana McCarthy

Found 21 papers, 2 papers with code

Towards Better Context-aware Lexical Semantics:Adjusting Contextualized Representations through Static Anchors

no code implementations EMNLP 2020 Qianchu Liu, Diana McCarthy, Anna Korhonen

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.

Semantic Data Set Construction from Human Clustering and Spatial Arrangement

no code implementations CL (ACL) 2021 Olga Majewska, Diana McCarthy, Jasper J. F. van den Bosch, Nikolaus Kriegeskorte, Ivan Vulić, Anna Korhonen

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.

Clustering Representation Learning +3

Measuring Context-Word Biases in Lexical Semantic Datasets

no code implementations13 Dec 2021 Qianchu Liu, Diana McCarthy, Anna Korhonen

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

1 code implementation EMNLP 2021 Qianchu Liu, Edoardo M. Ponti, Diana McCarthy, Ivan Vulić, Anna Korhonen

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

1 code implementation COLING 2020 Olga Majewska, Ivan Vuli{\'c}, Diana McCarthy, Anna Korhonen

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.

Clustering Multilingual NLP +1

Investigating Cross-Lingual Alignment Methods for Contextualized Embeddings with Token-Level Evaluation

no code implementations CONLL 2019 Qianchu Liu, Diana McCarthy, Ivan Vuli{\'c}, Anna Korhonen

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.

Retrieval Sentence +2

Semantic Clustering of Pivot Paraphrases

no code implementations LREC 2014 Marianna Apidianaki, Emilia Verzeni, Diana McCarthy

Paraphrases extracted from parallel corpora by the pivot method (Bannard and Callison-Burch, 2005) constitute a valuable resource for multilingual NLP applications.

Clustering Machine Translation +1

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