Backretrieval: An Image-Pivoted Evaluation Metric for Cross-Lingual Text Representations Without Parallel Corpora

• URL: http://arxiv.org/abs/2105.04971v1
• Date: Tue, 11 May 2021 12:14:24 GMT
• Title: Backretrieval: An Image-Pivoted Evaluation Metric for Cross-Lingual Text Representations Without Parallel Corpora
• Authors: Mikhail Fain, Niall Twomey and Danushka Bollegala
• Abstract summary: Backretrieval is shown to correlate with ground truth metrics on annotated datasets. Our experiments conclude with a case study on a recipe dataset without parallel cross-lingual data.
• Score: 19.02834713111249
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cross-lingual text representations have gained popularity lately and act as the backbone of many tasks such as unsupervised machine translation and cross-lingual information retrieval, to name a few. However, evaluation of such representations is difficult in the domains beyond standard benchmarks due to the necessity of obtaining domain-specific parallel language data across different pairs of languages. In this paper, we propose an automatic metric for evaluating the quality of cross-lingual textual representations using images as a proxy in a paired image-text evaluation dataset. Experimentally, Backretrieval is shown to highly correlate with ground truth metrics on annotated datasets, and our analysis shows statistically significant improvements over baselines. Our experiments conclude with a case study on a recipe dataset without parallel cross-lingual data. We illustrate how to judge cross-lingual embedding quality with Backretrieval, and validate the outcome with a small human study.

Related papers

• Cross-lingual Contextualized Phrase Retrieval [63.80154430930898]
  We propose a new task formulation of dense retrieval, cross-lingual contextualized phrase retrieval. We train our Cross-lingual Contextualized Phrase Retriever (CCPR) using contrastive learning. On the phrase retrieval task, CCPR surpasses baselines by a significant margin, achieving a top-1 accuracy that is at least 13 points higher.
  arXiv  Detail & Related papers  (2024-03-25T14:46:51Z)
• Automatic Data Retrieval for Cross Lingual Summarization [4.759360739268894]
  Cross-lingual summarization involves the summarization of text written in one language to a different one. In this work, we aim to perform cross-lingual summarization from English to Hindi.
  arXiv  Detail & Related papers  (2023-12-22T09:13:24Z)
• FRMT: A Benchmark for Few-Shot Region-Aware Machine Translation [64.9546787488337]
  We present FRMT, a new dataset and evaluation benchmark for Few-shot Region-aware Machine Translation. The dataset consists of professional translations from English into two regional variants each of Portuguese and Mandarin Chinese.
  arXiv  Detail & Related papers  (2022-10-01T05:02:04Z)
• Cross-Lingual Phrase Retrieval [49.919180978902915]
  Cross-lingual retrieval aims to retrieve relevant text across languages. Current methods typically achieve cross-lingual retrieval by learning language-agnostic text representations in word or sentence level. We propose XPR, a cross-lingual phrase retriever that extracts phrase representations from unlabeled example sentences.
  arXiv  Detail & Related papers  (2022-04-19T13:35:50Z)
• IGLUE: A Benchmark for Transfer Learning across Modalities, Tasks, and Languages [87.5457337866383]
  We introduce the Image-Grounded Language Understanding Evaluation benchmark. IGLUE brings together visual question answering, cross-modal retrieval, grounded reasoning, and grounded entailment tasks across 20 diverse languages. We find that translate-test transfer is superior to zero-shot transfer and that few-shot learning is hard to harness for many tasks.
  arXiv  Detail & Related papers  (2022-01-27T18:53:22Z)
• On Cross-Lingual Retrieval with Multilingual Text Encoders [51.60862829942932]
  We study the suitability of state-of-the-art multilingual encoders for cross-lingual document and sentence retrieval tasks. We benchmark their performance in unsupervised ad-hoc sentence- and document-level CLIR experiments. We evaluate multilingual encoders fine-tuned in a supervised fashion (i.e., we learn to rank) on English relevance data in a series of zero-shot language and domain transfer CLIR experiments.
  arXiv  Detail & Related papers  (2021-12-21T08:10:27Z)
• Does Summary Evaluation Survive Translation to Other Languages? [0.0]
  We translate an existing English summarization dataset, SummEval dataset, to four different languages. We analyze the scores from the automatic evaluation metrics in translated languages, as well as their correlation with human annotations in the source language.
  arXiv  Detail & Related papers  (2021-09-16T17:35:01Z)
• Cross-language Sentence Selection via Data Augmentation and Rationale Training [22.106577427237635]
  It uses data augmentation and negative sampling techniques on noisy parallel sentence data to learn a cross-lingual embedding-based query relevance model. Results show that this approach performs as well as or better than multiple state-of-the-art machine translation + monolingual retrieval systems trained on the same parallel data.
  arXiv  Detail & Related papers  (2021-06-04T07:08:47Z)
• MultiSubs: A Large-scale Multimodal and Multilingual Dataset [32.48454703822847]
  This paper introduces a large-scale multimodal and multilingual dataset that aims to facilitate research on grounding words to images in their contextual usage in language. The dataset consists of images selected to unambiguously illustrate concepts expressed in sentences from movie subtitles. We show the utility of the dataset on two automatic tasks: (i) fill-in-the blank; (ii) lexical translation.
  arXiv  Detail & Related papers  (2021-03-02T18:09:07Z)
• WikiLingua: A New Benchmark Dataset for Cross-Lingual Abstractive Summarization [41.578594261746055]
  We introduce WikiLingua, a large-scale, multilingual dataset for the evaluation of crosslingual abstractive summarization systems. We extract article and summary pairs in 18 languages from WikiHow, a high quality, collaborative resource of how-to guides on a diverse set of topics written by human authors. We create gold-standard article-summary alignments across languages by aligning the images that are used to describe each how-to step in an article.
  arXiv  Detail & Related papers  (2020-10-07T00:28:05Z)
• On the Language Neutrality of Pre-trained Multilingual Representations [70.93503607755055]
  We investigate the language-neutrality of multilingual contextual embeddings directly and with respect to lexical semantics. Our results show that contextual embeddings are more language-neutral and, in general, more informative than aligned static word-type embeddings. We show how to reach state-of-the-art accuracy on language identification and match the performance of statistical methods for word alignment of parallel sentences.
  arXiv  Detail & Related papers  (2020-04-09T19:50:32Z)

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.