Gareth Jones


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MultiMWE: Building a Multi-lingual Multi-Word Expression (MWE) Parallel Corpora
Lifeng Han | Gareth Jones | Alan Smeaton
Proceedings of the 12th Language Resources and Evaluation Conference

Multi-word expressions (MWEs) are a hot topic in research in natural language processing (NLP), including topics such as MWE detection, MWE decomposition, and research investigating the exploitation of MWEs in other NLP fields such as Machine Translation. However, the availability of bilingual or multi-lingual MWE corpora is very limited. The only bilingual MWE corpora that we are aware of is from the PARSEME (PARSing and Multi-word Expressions) EU Project. This is a small collection of only 871 pairs of English-German MWEs. In this paper, we present multi-lingual and bilingual MWE corpora that we have extracted from root parallel corpora. Our collections are 3,159,226 and 143,042 bilingual MWE pairs for German-English and Chinese-English respectively after filtering. We examine the quality of these extracted bilingual MWEs in MT experiments. Our initial experiments applying MWEs in MT show improved translation performances on MWE terms in qualitative analysis and better general evaluation scores in quantitative analysis, on both German-English and Chinese-English language pairs. We follow a standard experimental pipeline to create our MultiMWE corpora which are available online. Researchers can use this free corpus for their own models or use them in a knowledge base as model features.

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AlphaMWE: Construction of Multilingual Parallel Corpora with MWE Annotations
Lifeng Han | Gareth Jones | Alan Smeaton
Proceedings of the Joint Workshop on Multiword Expressions and Electronic Lexicons

In this work, we present the construction of multilingual parallel corpora with annotation of multiword expressions (MWEs). MWEs include verbal MWEs (vMWEs) defined in the PARSEME shared task that have a verb as the head of the studied terms. The annotated vMWEs are also bilingually and multilingually aligned manually. The languages covered include English, Chinese, Polish, and German. Our original English corpus is taken from the PARSEME shared task in 2018. We performed machine translation of this source corpus followed by human post editing and annotation of target MWEs. Strict quality control was applied for error limitation, i.e., each MT output sentence received first manual post editing and annotation plus second manual quality rechecking. One of our findings during corpora preparation is that accurate translation of MWEs presents challenges to MT systems. To facilitate further MT research, we present a categorisation of the error types encountered by MT systems in performing MWE related translation. To acquire a broader view of MT issues, we selected four popular state-of-the-art MT models for comparisons namely: Microsoft Bing Translator, GoogleMT, Baidu Fanyi and DeepL MT. Because of the noise removal, translation post editing and MWE annotation by human professionals, we believe our AlphaMWE dataset will be an asset for cross-lingual and multilingual research, such as MT and information extraction. Our multilingual corpora are available as open access at

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100,000 Podcasts: A Spoken English Document Corpus
Ann Clifton | Sravana Reddy | Yongze Yu | Aasish Pappu | Rezvaneh Rezapour | Hamed Bonab | Maria Eskevich | Gareth Jones | Jussi Karlgren | Ben Carterette | Rosie Jones
Proceedings of the 28th International Conference on Computational Linguistics

Podcasts are a large and growing repository of spoken audio. As an audio format, podcasts are more varied in style and production type than broadcast news, contain more genres than typically studied in video data, and are more varied in style and format than previous corpora of conversations. When transcribed with automatic speech recognition they represent a noisy but fascinating collection of documents which can be studied through the lens of natural language processing, information retrieval, and linguistics. Paired with the audio files, they are also a resource for speech processing and the study of paralinguistic, sociolinguistic, and acoustic aspects of the domain. We introduce the Spotify Podcast Dataset, a new corpus of 100,000 podcasts. We demonstrate the complexity of the domain with a case study of two tasks: (1) passage search and (2) summarization. This is orders of magnitude larger than previous speech corpora used for search and summarization. Our results show that the size and variability of this corpus opens up new avenues for research.

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An Investigative Study of Multi-Modal Cross-Lingual Retrieval
Piyush Arora | Dimitar Shterionov | Yasufumi Moriya | Abhishek Kaushik | Daria Dzendzik | Gareth Jones
Proceedings of the workshop on Cross-Language Search and Summarization of Text and Speech (CLSSTS2020)

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.


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Word-Node2Vec: Improving Word Embedding with Document-Level Non-Local Word Co-occurrences
Procheta Sen | Debasis Ganguly | Gareth Jones
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

A standard word embedding algorithm, such as word2vec and glove, makes a strong assumption that words are likely to be semantically related only if they co-occur locally within a window of fixed size. However, this strong assumption may not capture the semantic association between words that co-occur frequently but non-locally within documents. In this paper, we propose a graph-based word embedding method, named ‘word-node2vec’. By relaxing the strong constraint of locality, our method is able to capture both the local and non-local co-occurrences. Word-node2vec constructs a graph where every node represents a word and an edge between two nodes represents a combination of both local (e.g. word2vec) and document-level co-occurrences. Our experiments show that word-node2vec outperforms word2vec and glove on a range of different tasks, such as predicting word-pair similarity, word analogy and concept categorization.


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Development of an Annotated Multimodal Dataset for the Investigation of Classification and Summarisation of Presentations using High-Level Paralinguistic Features
Keith Curtis | Nick Campbell | Gareth Jones
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Tempo-Lexical Context Driven Word Embedding for Cross-Session Search Task Extraction
Procheta Sen | Debasis Ganguly | Gareth Jones
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

Task extraction is the process of identifying search intents over a set of queries potentially spanning multiple search sessions. Most existing research on task extraction has focused on identifying tasks within a single session, where the notion of a session is defined by a fixed length time window. By contrast, in this work we seek to identify tasks that span across multiple sessions. To identify tasks, we conduct a global analysis of a query log in its entirety without restricting analysis to individual temporal windows. To capture inherent task semantics, we represent queries as vectors in an abstract space. We learn the embedding of query words in this space by leveraging the temporal and lexical contexts of queries. Embedded query vectors are then clustered into tasks. Experiments demonstrate that task extraction effectiveness is improved significantly with our proposed method of query vector embedding in comparison to existing approaches that make use of documents retrieved from a collection to estimate semantic similarities between queries.


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Identifying Effective Translations for Cross-lingual Arabic-to-English User-generated Speech Search
Ahmad Khwileh | Haithem Afli | Gareth Jones | Andy Way
Proceedings of the Third Arabic Natural Language Processing Workshop

Cross Language Information Retrieval (CLIR) systems are a valuable tool to enable speakers of one language to search for content of interest expressed in a different language. A group for whom this is of particular interest is bilingual Arabic speakers who wish to search for English language content using information needs expressed in Arabic queries. A key challenge in CLIR is crossing the language barrier between the query and the documents. The most common approach to bridging this gap is automated query translation, which can be unreliable for vague or short queries. In this work, we examine the potential for improving CLIR effectiveness by predicting the translation effectiveness using Query Performance Prediction (QPP) techniques. We propose a novel QPP method to estimate the quality of translation for an Arabic-English Cross-lingual User-generated Speech Search (CLUGS) task. We present an empirical evaluation that demonstrates the quality of our method on alternative translation outputs extracted from an Arabic-to-English Machine Translation system developed for this task. Finally, we show how this framework can be integrated in CLUGS to find relevant translations for improved retrieval performance.


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Developing a Dataset for Evaluating Approaches for Document Expansion with Images
Debasis Ganguly | Iacer Calixto | Gareth Jones
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

Motivated by the adage that a “picture is worth a thousand words” it can be reasoned that automatically enriching the textual content of a document with relevant images can increase the readability of a document. Moreover, features extracted from the additional image data inserted into the textual content of a document may, in principle, be also be used by a retrieval engine to better match the topic of a document with that of a given query. In this paper, we describe our approach of building a ground truth dataset to enable further research into automatic addition of relevant images to text documents. The dataset is comprised of the official ImageCLEF 2010 collection (a collection of images with textual metadata) to serve as the images available for automatic enrichment of text, a set of 25 benchmark documents that are to be enriched, which in this case are children’s short stories, and a set of manually judged relevant images for each query story obtained by the standard procedure of depth pooling. We use this benchmark dataset to evaluate the effectiveness of standard information retrieval methods as simple baselines for this task. The results indicate that using the whole story as a weighted query, where the weight of each query term is its tf-idf value, achieves an precision of 0:1714 within the top 5 retrieved images on an average.


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DCU: Using Distributional Semantics and Domain Adaptation for the Semantic Textual Similarity SemEval-2015 Task 2
Piyush Arora | Chris Hokamp | Jennifer Foster | Gareth Jones
Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)


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Automatic Prediction of Aesthetics and Interestingness of Text Passages
Debasis Ganguly | Johannes Leveling | Gareth Jones
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers


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Cross-Lingual Topical Relevance Models
Debasis Ganguly | Johannes Leveling | Gareth Jones
Proceedings of COLING 2012

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Approximate Sentence Retrieval for Scalable and Efficient Example-Based Machine Translation
Johannes Leveling | Debasis Ganguly | Sandipan Dandapat | Gareth Jones
Proceedings of COLING 2012


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Domain-Specific Query Translation for Multilingual Information Access using Machine Translation Augmented With Dictionaries Mined from Wikipedia
Gareth Jones | Fabio Fantino | Eamonn Newman | Ying Zhang
Proceedings of the 2nd workshop on Cross Lingual Information Access (CLIA) Addressing the Information Need of Multilingual Societies


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Investigating Cross-Language Speech Retrieval for a Spontaneous Conversational Speech Collection
Diana Inkpen | Muath Alzghool | Gareth Jones | Douglas Oard
Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Short Papers