Search Results for author:
Found 8 papers, 5 papers with code
CALRec: Contrastive Alignment of Generative LLMs for Sequential Recommendation
Building upon the success of Large Language Models (LLMs) in a variety of tasks, researchers have recently explored using LLMs that are pretrained on vast corpora of text for sequential recommendation.
Self-Augmented In-Context Learning for Unsupervised Word Translation
Recent work has shown that, while large language models (LLMs) demonstrate strong word translation or bilingual lexicon induction (BLI) capabilities in few-shot setups, they still cannot match the performance of 'traditional' mapping-based approaches in the unsupervised scenario where no seed translation pairs are available, especially for lower-resource languages.
Bilingual Lexicon Induction
Cross-Lingual Word Embeddings
+9
On Bilingual Lexicon Induction with Large Language Models
Bilingual Lexicon Induction (BLI) is a core task in multilingual NLP that still, to a large extent, relies on calculating cross-lingual word representations.
Bilingual Lexicon Induction
Cross-Lingual Word Embeddings
+8
Translation-Enhanced Multilingual Text-to-Image Generation
Research on text-to-image generation (TTI) still predominantly focuses on the English language due to the lack of annotated image-caption data in other languages; in the long run, this might widen inequitable access to TTI technology.
Cross-lingual Text-to-Image Generation
Crosslingual Text-to-Image Generation
+6
Improving Bilingual Lexicon Induction with Cross-Encoder Reranking
This crucial step is done via 1) creating a word similarity dataset, comprising positive word pairs (i. e., true translations) and hard negative pairs induced from the original CLWE space, and then 2) fine-tuning an mPLM (e. g., mBERT or XLM-R) in a cross-encoder manner to predict the similarity scores.
Bilingual Lexicon Induction
Cross-Lingual Word Embeddings
+7
Improving Word Translation via Two-Stage Contrastive Learning
At Stage C1, we propose to refine standard cross-lingual linear maps between static word embeddings (WEs) via a contrastive learning objective; we also show how to integrate it into the self-learning procedure for even more refined cross-lingual maps.
Emergent Communication Pretraining for Few-Shot Machine Translation
On the other hand, this also provides an extrinsic evaluation protocol to probe the properties of emergent languages ex vitro.
Two-Headed Monster And Crossed Co-Attention Networks
This paper presents some preliminary investigations of a new co-attention mechanism in neural transduction models.
