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Found 13 papers, 3 papers with code
A Systematic Study of Inner-Attention-Based Sentence Representations in Multilingual Neural Machine Translation
In particular, we show that larger intermediate layers not only improve translation quality, especially for long sentences, but also push the accuracy of trainable classification tasks.
An Evaluation Benchmark for Testing the Word Sense Disambiguation Capabilities of Machine Translation Systems
Lexical ambiguity is one of the many challenging linguistic phenomena involved in translation, i. e., translating an ambiguous word with its correct sense.
An Evaluation of Language-Agnostic Inner-Attention-Based Representations in Machine Translation
In this paper, we explore a multilingual translation model with a cross-lingually shared layer that can be used as fixed-size sentence representation in different downstream tasks.
The MuCoW Test Suite at WMT 2019: Automatically Harvested Multilingual Contrastive Word Sense Disambiguation Test Sets for Machine Translation
Supervised Neural Machine Translation (NMT) systems currently achieve impressive translation quality for many language pairs.
An Analysis of Encoder Representations in Transformer-Based Machine Translation
We assess the representations of the encoder by extracting dependency relations based on self-attention weights, we perform four probing tasks to study the amount of syntactic and semantic captured information and we also test attention in a transfer learning scenario.
The University of Helsinki submissions to the WMT18 news task
This paper describes the University of Helsinki{'}s submissions to the WMT18 shared news translation task for English-Finnish and English-Estonian, in both directions.
SupWSD: A Flexible Toolkit for Supervised Word Sense Disambiguation
In this demonstration we present SupWSD, a Java API for supervised Word Sense Disambiguation (WSD).
Neural Sequence Learning Models for Word Sense Disambiguation
Word Sense Disambiguation models exist in many flavors.
Ranked #19 on
Word Sense Disambiguation
on Supervised:
Sew-Embed at SemEval-2017 Task 2: Language-Independent Concept Representations from a Semantically Enriched Wikipedia
This paper describes Sew-Embed, our language-independent approach to multilingual and cross-lingual semantic word similarity as part of the SemEval-2017 Task 2.
EuroSense: Automatic Harvesting of Multilingual Sense Annotations from Parallel Text
Parallel corpora are widely used in a variety of Natural Language Processing tasks, from Machine Translation to cross-lingual Word Sense Disambiguation, where parallel sentences can be exploited to automatically generate high-quality sense annotations on a large scale.
Word Sense Disambiguation: A Unified Evaluation Framework and Empirical Comparison
In this paper we develop a unified evaluation framework and analyze the performance of various Word Sense Disambiguation systems in a fair setup.
Ranked #4 on
Word Sense Disambiguation
on Knowledge-based:
Semantic Indexing of Multilingual Corpora and its Application on the History Domain
The increasing amount of multilingual text collections available in different domains makes its automatic processing essential for the development of a given field.
Entity Linking meets Word Sense Disambiguation: a Unified Approach
Entity Linking (EL) and Word Sense Disambiguation (WSD) both address the lexical ambiguity of language.
Ranked #3 on
Word Sense Disambiguation
on Knowledge-based:
