Semanlink - Multilingual NLP

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Multilingual NLP

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9 Documents (Liste longue)

Omar Khattab sur X : "ColBERT in 81 languages by generalizing from English training! ..."

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2024-02-28 A propos

Rachit Bansal sur X : "An LLM can be efficiently *composed* with specialized (L)LMs to enable new tasks"

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2024-01-06 A propos

[2305.11778] Cross-Lingual Supervision improves Large Language Models Pre-training

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2023-05-22 A propos

Document AI: LiLT a better language agnostic LayoutLM model

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2022-11-22 A propos

The State of Multilingual AI

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2022-11-14 A propos

Domain transfer with GGPL: German Generative Pseudo Labeling 🥨 | by Matthias Richter | Jun, 2022 | ML6team

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2022-06-02 A propos

[2110.08151] mLUKE: The Power of Entity Representations in Multilingual Pretrained Language Models

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[Ikuya Yamada sur Twitter : "Is entity representation effective to improve multilingual language models?..."](doc:2022/04/ikuya_yamada_sur_twitter_is_)

> Recent studies have shown that multilingual pretrained language models can be effectively improved with cross-lingual alignment information from Wikipedia entities. However, **existing methods only exploit entity information in pretraining and do not explicitly use entities in downstream tasks**. In this study, we explore the **effectiveness of leveraging entity representations for downstream cross-lingual tasks**.
>
> the key insight is that incorporating entity representations into the input allows us to extract more language-agnostic features.

[Github](https://github.com/studio-ousia/luke)

> Entity representations are known to enhance
language models in mono-lingual settings
(Zhang et al., 2019: [ERNIE](tag:ernie.html); Peters et al., 2019:  [[1909.04164] Knowledge Enhanced Contextual Word Representations](doc:2020/05/1909_04164_knowledge_enhanced); Wang et al.,
2021 [[1911.06136] KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation](doc:2020/11/1911_06136_kepler_a_unified_); Xiong et al., 2020; Yamada et al., 2020: [[2010.01057] LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention](doc:2020/11/2010_01057_luke_deep_context))
presumably by introducing real-world knowledge.
We show that using entity representations facilitates
cross-lingual transfer by providing languageindependent
features.
>
> Multilingual extension of LUKE. The model is trained with the multilingual
masked language modeling (MLM) task as well
as the masked entity prediction (MEP) task with
Wikipedia entity embeddings

> We investigate two ways of using the entity representations
in cross-lingual transfer tasks:
> 1. perform
entity linking for the input text, and append
the detected entity tokens to the input sequence.
The entity tokens are expected to provide language independent
features to the model
> 2. use the entity
[MASK] token from the MEP task as a languageindependent
feature extractor.

2022-04-17 A propos

[2004.09813] Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation

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2022-03-18 A propos

Why You Should Do NLP Beyond English

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2020-08-01 A propos

Propriétés

sl:creationDate : 2020-08-01
sl:creationTime : 2020-08-01T18:54:00Z
rdf:type : sl:Tag
skos:prefLabel : Multilingual NLP