Utilizing Language-Image Pretraining for Efficient and Robust Bilingual Word Alignment

• URL: http://arxiv.org/abs/2205.11616v1
• Date: Mon, 23 May 2022 20:29:26 GMT
• Title: Utilizing Language-Image Pretraining for Efficient and Robust Bilingual Word Alignment
• Authors: Tuan Dinh, Jy-yong Sohn, Shashank Rajput, Timothy Ossowski, Yifei Ming, Junjie Hu, Dimitris Papailiopoulos, Kangwook Lee
• Abstract summary: We develop a novel UWT method dubbed Word Alignment using Language-Image Pretraining (WALIP) WALIP uses visual observations via the shared embedding space of images and texts provided by CLIP models. Our experiments show that WALIP improves upon the state-of-the-art performance of bilingual word alignment for a few language pairs.
• Score: 27.405171616881322
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Word translation without parallel corpora has become feasible, rivaling the performance of supervised methods. Recent findings have shown that the accuracy and robustness of unsupervised word translation (UWT) can be improved by making use of visual observations, which are universal representations across languages. In this work, we investigate the potential of using not only visual observations but also pretrained language-image models for enabling a more efficient and robust UWT. Specifically, we develop a novel UWT method dubbed Word Alignment using Language-Image Pretraining (WALIP), which leverages visual observations via the shared embedding space of images and texts provided by CLIP models (Radford et al., 2021). WALIP has a two-step procedure. First, we retrieve word pairs with high confidences of similarity, computed using our proposed image-based fingerprints, which define the initial pivot for the word alignment. Second, we apply our robust Procrustes algorithm to estimate the linear mapping between two embedding spaces, which iteratively corrects and refines the estimated alignment. Our extensive experiments show that WALIP improves upon the state-of-the-art performance of bilingual word alignment for a few language pairs across different word embeddings and displays great robustness to the dissimilarity of language pairs or training corpora for two word embeddings.

Related papers

• VECO 2.0: Cross-lingual Language Model Pre-training with Multi-granularity Contrastive Learning [56.47303426167584]
  We propose a cross-lingual pre-trained model VECO2.0 based on contrastive learning with multi-granularity alignments. Specifically, the sequence-to-sequence alignment is induced to maximize the similarity of the parallel pairs and minimize the non-parallel pairs. token-to-token alignment is integrated to bridge the gap between synonymous tokens excavated via the thesaurus dictionary from the other unpaired tokens in a bilingual instance.
  arXiv  Detail & Related papers  (2023-04-17T12:23:41Z)
• DetCLIPv2: Scalable Open-Vocabulary Object Detection Pre-training via Word-Region Alignment [104.54362490182335]
  DetCLIPv2 is an efficient training framework that incorporates large-scale image-text pairs to achieve open-vocabulary object detection. DetCLIPv2 directly learns the fine-grained word-region alignment from massive image-text pairs in an end-to-end manner. With 13M image-text pairs for pre-training, DetCLIPv2 demonstrates superior open-vocabulary detection performance.
  arXiv  Detail & Related papers  (2023-04-10T11:08:15Z)
• Robust Unsupervised Cross-Lingual Word Embedding using Domain Flow Interpolation [48.32604585839687]
  Previous adversarial approaches have shown promising results in inducing cross-lingual word embedding without parallel data. We propose to make use of a sequence of intermediate spaces for smooth bridging.
  arXiv  Detail & Related papers  (2022-10-07T04:37:47Z)
• Unsupervised Alignment of Distributional Word Embeddings [0.0]
  Cross-domain alignment play a key role in tasks ranging from machine translation to transfer learning. We show that the proposed approach achieves good performance on the bilingual lexicon induction task across several language pairs.
  arXiv  Detail & Related papers  (2022-03-09T16:39:06Z)
• Combining Static Word Embeddings and Contextual Representations for Bilingual Lexicon Induction [19.375597786174197]
  We propose a simple yet effective mechanism to combine the static word embeddings and the contextual representations. We test the combination mechanism on various language pairs under the supervised and unsupervised BLI benchmark settings.
  arXiv  Detail & Related papers  (2021-06-06T10:31:02Z)
• SLUA: A Super Lightweight Unsupervised Word Alignment Model via Cross-Lingual Contrastive Learning [79.91678610678885]
  We propose a super lightweight unsupervised word alignment model (SLUA) Experimental results on several public benchmarks demonstrate that our model achieves competitive, if not better, performance. Notably, we recognize our model as a pioneer attempt to unify bilingual word embedding and word alignments.
  arXiv  Detail & Related papers  (2021-02-08T05:54:11Z)
• Word Alignment by Fine-tuning Embeddings on Parallel Corpora [96.28608163701055]
  Word alignment over parallel corpora has a wide variety of applications, including learning translation lexicons, cross-lingual transfer of language processing tools, and automatic evaluation or analysis of translation outputs. Recently, other work has demonstrated that pre-trained contextualized word embeddings derived from multilingually trained language models (LMs) prove an attractive alternative, achieving competitive results on the word alignment task even in the absence of explicit training on parallel data. In this paper, we examine methods to marry the two approaches: leveraging pre-trained LMs but fine-tuning them on parallel text with objectives designed to improve alignment quality, and proposing
  arXiv  Detail & Related papers  (2021-01-20T17:54:47Z)
• Learning Contextualised Cross-lingual Word Embeddings and Alignments for Extremely Low-Resource Languages Using Parallel Corpora [63.5286019659504]
  We propose a new approach for learning contextualised cross-lingual word embeddings based on a small parallel corpus. Our method obtains word embeddings via an LSTM encoder-decoder model that simultaneously translates and reconstructs an input sentence.
  arXiv  Detail & Related papers  (2020-10-27T22:24:01Z)
• Multilingual Alignment of Contextual Word Representations [49.42244463346612]
  BERT exhibits significantly improved zero-shot performance on XNLI compared to the base model. We introduce a contextual version of word retrieval and show that it correlates well with downstream zero-shot transfer. These results support contextual alignment as a useful concept for understanding large multilingual pre-trained models.
  arXiv  Detail & Related papers  (2020-02-10T03:27:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.