An Investigative Study of Multi-Modal Cross-Lingual Retrieval

Piyush Arora, Dimitar Shterionov, Yasufumi Moriya, Abhishek Kaushik, Daria Dzendzik, Gareth Jones

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Abstract

We describe work from our investigations of the novel area of multi-modal cross-lingual retrieval (MMCLIR) under low-resource conditions. We study the challenges associated with MMCLIR relating to: (i) data conversion between different modalities, for example speech and text, (ii) overcoming the language barrier between source and target languages; (iii) effectively scoring and ranking documents to suit the retrieval task; and (iv) handling low resource constraints that prohibit development of heavily tuned machine translation (MT) and automatic speech recognition (ASR) systems. We focus on the use case of retrieving text and speech documents in Swahili, using English queries which was the main focus of the OpenCLIR shared task. Our work is developed within the scope of this task. In this paper we devote special attention to the automatic translation (AT) component which is crucial for the overall quality of the MMCLIR system. We exploit a combination of dictionaries and phrase-based statistical machine translation (MT) systems to tackle effectively the subtask of query translation. We address each MMCLIR challenge individually, and develop separate components for automatic translation (AT), speech processing (SP) and information retrieval (IR). We find that results with respect to cross-lingual text retrieval are quite good relative to the task of cross-lingual speech retrieval. Overall we find that the task of MMCLIR and specifically cross-lingual speech retrieval is quite complex. Further we pinpoint open issues related to handling cross-lingual audio and text retrieval for low resource languages that need to be addressed in future research.
Original languageEnglish
Title of host publicationProceedings of the workshop on Cross-Language Search and Summarization of Text and Speech (CLSSTS2020)
Place of PublicationMarseille, France
PublisherEuropean Language Resources Association (ELRA)
Pages58-67
Number of pages10
ISBN (Print)9791095546559
Publication statusPublished - 1 May 2020
Externally publishedYes

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