WiC-TSV-de: German Word-in-Context Target-Sense-Verification Dataset and Cross-Lingual Transfer Analysis
Target Sense Verification (TSV) describes the binary disambiguation task of deciding whether the intended sense of a target word in a context corresponds to a given target sense. In this paper, we introduce WiC-TSV-de, a multi-domain dataset for German Target Sense Verification. While the training and development sets consist of domain-independent instances only, the test set contains domain-bound subsets, originating from four different domains, being Gastronomy, Medicine, Hunting, and Zoology. The domain-bound subsets incorporate adversarial examples such as in-domain ambiguous target senses and context-mixing (i.e., using the target sense in an out-of-domain context) which contribute to the challenging nature of the presented dataset. WiC-TSV-de allows for the development of sense-inventory-independent disambiguation models that can generalise their knowledge for different domain settings. By combining it with the original English WiC-TSV benchmark, we performed monolingual and cross-lingual analysis, where the evaluated baseline models were not able to solve the dataset to a satisfying degree, leaving a big gap to human performance.
Code (0)
등록된 구현이 없습니다.
Tasks
Cross-Lingual TransferSimilar Papers 제목 키워드 기반
SenseFitting: Sense Level Semantic Specialization of Word Embeddings for Word Sense Disambiguation
We introduce a neural network-based system of Word Sense Disambiguation (WSD) for German that is based on SenseFitting, a novel method for optimizing WSD. We outperform knowledge-based WSD methods by up to 25% F1-score a…
LEMMAWord EmbeddingsWord Sense DisambiguationDiaSense at SemEval-2020 Task 1: Modeling Sense Change via Pre-trained BERT Embeddings
This paper describes DiaSense, a system developed for Task 1 {`}Unsupervised Lexical Semantic Change Detection{'} of SemEval 2020. In DiaSense, contextualized word embeddings are used to model word sense changes. This al…
Change DetectionWord EmbeddingsCTLR@WiC-TSV: Target Sense Verification using Marked Inputs andPre-trained Models
This paper describes the CTRL participation in the Target Sense Verification of the Words in Context challenge (WiC-TSV) at SemDeep6. Our strategy is based on a simplistic annotation scheme of the target words to later b…
Entity LinkingPositionWord Sense Disambiguation with Transformer Models
In this paper, we tackle the task of Word Sense Disambiguation (WSD). We present our system submitted to the Word-in-Context Target Sense Verification challenge, part of the SemDeep workshop at IJCAI 2020 (Breit et al., …
Entity LinkingWord Sense DisambiguationCan Word Sense Distribution Detect Semantic Changes of Words?
Semantic Change Detection (SCD) of words is an important task for various NLP applications that must make time-sensitive predictions. Some words are used over time in novel ways to express new meanings, and these new mea…
Change DetectionWord Sense Disambiguation