Topic-based mixture language modelling

This paper describes an approach for constructing a mixture of language models based on simple statistical notions of semantics using probabilistic models developed for information retrieval. The approach encapsulates corpus-derived semantic information and is able to model varying styles of text. Using such information, the corpus texts are clustered in an unsupervised manner and a mixture of topic-specific language models is automatically created. The principal contribution of this work is to characterise the document space resulting from information retrieval techniques and to demonstrate the approach for mixture language modelling. A comparison is made between manual and automatic clustering in order to elucidate how the global content information is expressed in the space. We also compare (in terms of association with manual clustering and language modelling accuracy) alternative term-weighting schemes and the effect of singular value decomposition dimension reduction (latent semantic analysis). Test set perplexity results using the British National Corpus indicate that the approach can improve the potential of statistical language modelling. Using an adaptive procedure, the conventional model may be tuned to track text data with a slight increase in computational cost

Parameter-Efficient Neural Reranking for Cross-Lingual and Multilingual Retrieval

State-of-the-art neural (re)rankers are notoriously data hungry which - given the lack of large-scale training data in languages other than English - makes them rarely used in multilingual and cross-lingual retrieval settings. Current approaches therefore typically transfer rankers trained on English data to other languages and cross-lingual setups by means of multilingual encoders: they fine-tune all the parameters of a pretrained massively multilingual Transformer (MMT, e.g., multilingual BERT) on English relevance judgments and then deploy it in the target language. In this work, we show that two parameter-efficient approaches to cross-lingual transfer, namely Sparse Fine-Tuning Masks (SFTMs) and Adapters, allow for a more lightweight and more effective zero-shot transfer to multilingual and cross-lingual retrieval tasks. We first train language adapters (or SFTMs) via Masked Language Modelling and then train retrieval (i.e., reranking) adapters (SFTMs) on top while keeping all other parameters fixed. At inference, this modular design allows us to compose the ranker by applying the task adapter (or SFTM) trained with source language data together with the language adapter (or SFTM) of a target language. Besides improved transfer performance, these two approaches offer faster ranker training, with only a fraction of parameters being updated compared to full MMT fine-tuning. We benchmark our models on the CLEF-2003 benchmark, showing that our parameter-efficient methods outperform standard zero-shot transfer with full MMT fine-tuning, while enabling modularity and reducing training times. Further, we show on the example of Swahili and Somali that, for low(er)-resource languages, our parameter-efficient neural re-rankers can improve the ranking of the competitive machine translation-based ranker

Mono- and cross-lingual paraphrased text reuse and extrinsic plagiarism detection

Text reuse is the act of borrowing text (either verbatim or paraphrased) from an earlier written text. It could occur within the same language (mono-lingual) or across languages (cross-lingual) where the reused text is in a different language than the original text. Text reuse and its related problem, plagiarism (the unacknowledged reuse of text), are becoming serious issues in many fields and research shows that paraphrased and especially the cross-lingual cases of reuse are much harder to detect. Moreover, the recent rise in readily available multi-lingual content on the Web and social media has increased the problem to an unprecedented scale. To develop, compare, and evaluate automatic methods for mono- and crosslingual text reuse and extrinsic (finding portion(s) of text that is reused from the original text) plagiarism detection, standard evaluation resources are of utmost importance. However, previous efforts on developing such resources have mostly focused on English and some other languages. On the other hand, the Urdu language, which is widely spoken and has a large digital footprint, lacks resources in terms of core language processing tools and corpora. With this consideration in mind, this PhD research focuses on developing standard evaluation corpora, methods, and supporting resources to automatically detect mono-lingual (Urdu) and cross-lingual (English-Urdu) cases of text reuse and extrinsic plagiarism This thesis contributes a mono-lingual (Urdu) text reuse corpus (COUNTER Corpus) that contains real cases of Urdu text reuse at document-level. Another contribution is the development of a mono-lingual (Urdu) extrinsic plagiarism corpus (UPPC Corpus) that contains simulated cases of Urdu paraphrase plagiarism. Evaluation results, by applying a wide range of state-of-the-art mono-lingual methods on both corpora, shows that it is easier to detect verbatim cases than paraphrased ones. Moreover, the performance of these methods decreases considerably on real cases of reuse. A couple of supporting resources are also created to assist methods used in the cross-lingual (English-Urdu) text reuse detection. A large-scale multi-domain English-Urdu parallel corpus (EUPC-20) that contains parallel sentences is mined from the Web and several bi-lingual (English-Urdu) dictionaries are compiled using multiple approaches from different sources. Another major contribution of this study is the development of a large benchmark cross-lingual (English-Urdu) text reuse corpus (TREU Corpus). It contains English to Urdu real cases of text reuse at the document-level. A diversified range of methods are applied on the TREU Corpus to evaluate its usefulness and to show how it can be utilised in the development of automatic methods for measuring cross-lingual (English-Urdu) text reuse. A new cross-lingual method is also proposed that uses bilingual word embeddings to estimate the degree of overlap amongst text documents by computing the maximum weighted cosine similarity between word pairs. The overall low evaluation results indicate that it is a challenging task to detect crosslingual real cases of text reuse, especially when the language pairs have unrelated scripts, i.e., English-Urdu. However, an improvement in the result is observed using a combination of methods used in the experiments. The research work undertaken in this PhD thesis contributes corpora, methods, and supporting resources for the mono- and cross-lingual text reuse and extrinsic plagiarism for a significantly under-resourced Urdu and English-Urdu language pair. It highlights that paraphrased and cross-lingual cross-script real cases of text reuse are harder to detect and are still an open issue. Moreover, it emphasises the need to develop standard evaluation and supporting resources for under-resourced languages to facilitate research in these languages. The resources that have been developed and methods proposed could serve as a framework for future research in other languages and language pairs

Temporal Information Models for Real-Time Microblog Search

Real-time search in Twitter and other social media services is often biased towards the most recent results due to the “in the moment” nature of topic trends and their ephemeral relevance to users and media in general. However, “in the moment”, it is often difficult to look at all emerging topics and single-out the important ones from the rest of the social media chatter. This thesis proposes to leverage on external sources to estimate the duration and burstiness of live Twitter topics. It extends preliminary research where itwas shown that temporal re-ranking using external sources could indeed improve the accuracy of results. To further explore this topic we pursued three significant novel approaches: (1) multi-source information analysis that explores behavioral dynamics of users, such as Wikipedia live edits and page view streams, to detect topic trends and estimate the topic interest over time; (2) efficient methods for federated query expansion towards the improvement of query meaning; and (3) exploiting multiple sources towards the detection of temporal query intent. It differs from past approaches in the sense that it will work over real-time queries, leveraging on live user-generated content. This approach contrasts with previous methods that require an offline preprocessing step