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Found 16 papers, 4 papers with code
With More Contexts Comes Better Performance: Contextualized Sense Embeddings for All-Round Word Sense Disambiguation
Contextualized word embeddings have been employed effectively across several tasks in Natural Language Processing, as they have proved to carry useful semantic information.
Ranked #11 on
Word Sense Disambiguation
on Supervised:
IR like a SIR: Sense-enhanced Information Retrieval for Multiple Languages
With the advent of contextualized embeddings, attention towards neural ranking approaches for Information Retrieval increased considerably.
Reducing Disambiguation Biases in NMT by Leveraging Explicit Word Sense Information
Recent studies have shed some light on a common pitfall of Neural Machine Translation (NMT) models, stemming from their struggle to disambiguate polysemous words without lapsing into their most frequently occurring senses in the training corpus. In this paper, we first provide a novel approach for automatically creating high-precision sense-annotated parallel corpora, and then put forward a specifically tailored fine-tuning strategy for exploiting these sense annotations during training without introducing any additional requirement at inference time. The use of explicit senses proved to be beneficial to reduce the disambiguation bias of a baseline NMT model, while, at the same time, leading our system to attain higher BLEU scores than its vanilla counterpart in 3 language pairs.
FairLex: A Multilingual Benchmark for Evaluating Fairness in Legal Text Processing
We present a benchmark suite of four datasets for evaluating the fairness of pre-trained language models and the techniques used to fine-tune them for downstream tasks.
Wikipedia Entities as Rendezvous across Languages: Grounding Multilingual Language Models by Predicting Wikipedia Hyperlinks
Further adding extra languages lead to improvements in most tasks up to a certain point, but overall we found it non-trivial to scale improvements in model transferability by training on ever increasing amounts of Wikipedia languages.
ESC: Redesigning WSD with Extractive Sense Comprehension
By means of an extensive array of experiments, we show that ESC unleashes the full potential of our model, leading it to outdo all of its competitors and to set a new state of the art on the English WSD task.
Ranked #4 on
Word Sense Disambiguation
on Supervised:
XL-WiC: A Multilingual Benchmark for Evaluating Semantic Contextualization
The ability to correctly model distinct meanings of a word is crucial for the effectiveness of semantic representation techniques.
CluBERT: A Cluster-Based Approach for Learning Sense Distributions in Multiple Languages
Knowing the Most Frequent Sense (MFS) of a word has been proved to help Word Sense Disambiguation (WSD) models significantly.
Sense-Annotated Corpora for Word Sense Disambiguation in Multiple Languages and Domains
This limits the range of action of deep-learning approaches, which today are at the base of any NLP task and are hungry for data.
Just ``OneSeC'' for Producing Multilingual Sense-Annotated Data
The well-known problem of knowledge acquisition is one of the biggest issues in Word Sense Disambiguation (WSD), where annotated data are still scarce in English and almost absent in other languages.
SemEval-2018 Task 9: Hypernym Discovery
This paper describes the SemEval 2018 Shared Task on Hypernym Discovery.
Huge Automatically Extracted Training Sets for Multilingual Word Sense Disambiguation
We release to the community six large-scale sense-annotated datasets in multiple language to pave the way for supervised multilingual Word Sense Disambiguation.
A Short Survey on Sense-Annotated Corpora
Large sense-annotated datasets are increasingly necessary for training deep supervised systems in Word Sense Disambiguation.
Train-O-Matic: Large-Scale Supervised Word Sense Disambiguation in Multiple Languages without Manual Training Data
Annotating large numbers of sentences with senses is the heaviest requirement of current Word Sense Disambiguation.
