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Found 4 papers, 2 papers with code
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.
DiBiMT: A Novel Benchmark for Measuring Word Sense Disambiguation Biases in Machine Translation
Lexical ambiguity poses one of the greatest challenges in the field of Machine Translation.
WikiNEuRal: Combined Neural and Knowledge-based Silver Data Creation for Multilingual NER
Multilingual Named Entity Recognition (NER) is a key intermediate task which is needed in many areas of NLP.
