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Zifan Jiang

5 accepted papers

2025

ConLoan: A Contrastive Multilingual Dataset for Evaluating Loanwords

ACL 2025long

Lexical borrowing, the adoption of words from one language into another, is a ubiquitous linguistic phenomenon influenced by geopolitical, societal, and technological factors. This paper introduces ConLoan–a novel contrastive dataset comprising sentences with and without loanwords across 10 language…

2024

SignCLIP: Connecting Text and Sign Language by Contrastive Learning

EMNLP 2024main

We present SignCLIP, which re-purposes CLIP (Contrastive Language-Image Pretraining) to project spoken language text and sign language videos, two classes of natural languages of distinct modalities, into the same space. SignCLIP is an efficient method of learning useful visual representations for s…

2024

SwissSLi: The Multi-parallel Sign Language Corpus for Switzerland

COLING 2024main

In this work, we introduce SwissSLi, the first sign language corpus that contains parallel data of all three Swiss sign languages, namely Swiss German Sign Language (DSGS), French Sign Language of Switzerland (LSF-CH), and Italian Sign Language of Switzerland (LIS-CH). The data underlying this corpu…

Cited by 1SourcePDFScholar
2023

Considerations for meaningful sign language machine translation based on glosses

ACL 2023short

Automatic sign language processing is gaining popularity in Natural Language Processing (NLP) research (Yin et al., 2021). In machine translation (MT) in particular, sign language translation based on glosses is a prominent approach. In this paper, we review recent works on neural gloss translation.…

Cited by 40SourcePDFScholar
2023

Linguistically Motivated Sign Language Segmentation

EMNLP 2023long findings

Sign language segmentation is a crucial task in sign language processing systems. It enables downstream tasks such as sign recognition, transcription, and machine translation. In this work, we consider two kinds of segmentation: segmentation into individual signs and segmentation into \textit{phrase…

Cited by 0SourcecodeScholar