Monica Lestari Paramita

Also published as: Monica Paramita


SNuC: The Sheffield Numbers Spoken Language Corpus
Emma Barker | Jon Barker | Robert Gaizauskas | Ning Ma | Monica Lestari Paramita
Proceedings of the Thirteenth Language Resources and Evaluation Conference

We present SNuC, the first published corpus of spoken alphanumeric identifiers of the sort typically used as serial and part numbers in the manufacturing sector. The dataset contains recordings and transcriptions of over 50 native British English speakers, speaking over 13,000 multi-character alphanumeric sequences and totalling almost 20 hours of recorded speech. We describe requirements taken into account in the designing the corpus and the methodology used to construct it. We present summary statistics describing the corpus contents, as well as a preliminary investigation into errors in spoken alphanumeric identifiers. We validate the corpus by showing how it can be used to adapt a deep learning neural network based ASR system, resulting in improved recognition accuracy on the task of spoken alphanumeric identifier recognition. Finally, we discuss further potential uses for the corpus and for the tools developed to construct it.


What’s the Issue Here?: Task-based Evaluation of Reader Comment Summarization Systems
Emma Barker | Monica Paramita | Adam Funk | Emina Kurtic | Ahmet Aker | Jonathan Foster | Mark Hepple | Robert Gaizauskas
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

Automatic summarization of reader comments in on-line news is an extremely challenging task and a capability for which there is a clear need. Work to date has focussed on producing extractive summaries using well-known techniques imported from other areas of language processing. But are extractive summaries of comments what users really want? Do they support users in performing the sorts of tasks they are likely to want to perform with reader comments? In this paper we address these questions by doing three things. First, we offer a specification of one possible summary type for reader comment, based on an analysis of reader comment in terms of issues and viewpoints. Second, we define a task-based evaluation framework for reader comment summarization that allows summarization systems to be assessed in terms of how well they support users in a time-limited task of identifying issues and characterising opinion on issues in comments. Third, we describe a pilot evaluation in which we used the task-based evaluation framework to evaluate a prototype reader comment clustering and summarization system, demonstrating the viability of the evaluation framework and illustrating the sorts of insight such an evaluation affords.

The SENSEI Annotated Corpus: Human Summaries of Reader Comment Conversations in On-line News
Emma Barker | Monica Lestari Paramita | Ahmet Aker | Emina Kurtic | Mark Hepple | Robert Gaizauskas
Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue

Automatic label generation for news comment clusters
Ahmet Aker | Monica Paramita | Emina Kurtic | Adam Funk | Emma Barker | Mark Hepple | Rob Gaizauskas
Proceedings of the 9th International Natural Language Generation conference


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Assigning Terms to Domains by Document Classification
Robert Gaizauskas | Emma Barker | Monica Lestari Paramita | Ahmet Aker
Proceedings of the 4th International Workshop on Computational Terminology (Computerm)

Bootstrapping Term Extractors for Multiple Languages
Ahmet Aker | Monica Paramita | Emma Barker | Robert Gaizauskas
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

Terminology extraction resources are needed for a wide range of human language technology applications, including knowledge management, information extraction, semantic search, cross-language information retrieval and automatic and assisted translation. We create a low cost method for creating terminology extraction resources for 21 non-English EU languages. Using parallel corpora and a projection method, we create a General POS Tagger for these languages. We also investigate the use of EuroVoc terms and Wikipedia corpus to automatically create term grammar for each language. Our results show that these automatically generated resources can assist term extraction process with similar performance to manually generated resources. All resources resulted in this experiment are freely available for download.

Bilingual dictionaries for all EU languages
Ahmet Aker | Monica Paramita | Mārcis Pinnis | Robert Gaizauskas
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

Bilingual dictionaries can be automatically generated using the GIZA++ tool. However, these dictionaries contain a lot of noise, because of which the quality of outputs of tools relying on the dictionaries are negatively affected. In this work we present three different methods for cleaning noise from automatically generated bilingual dictionaries: LLR, pivot and translation based approach. We have applied these approaches on the GIZA++ dictionaries -- dictionaries covering official EU languages -- in order to remove noise. Our evaluation showed that all methods help to reduce noise. However, the best performance is achieved using the transliteration based approach. We provide all bilingual dictionaries (the original GIZA++ dictionaries and the cleaned ones) free for download. We also provide the cleaning tools and scripts for free download.


Extracting bilingual terminologies from comparable corpora
Ahmet Aker | Monica Paramita | Rob Gaizauskas
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)


Correlation between Similarity Measures for Inter-Language Linked Wikipedia Articles
Monica Lestari Paramita | Paul Clough | Ahmet Aker | Robert Gaizauskas
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

Wikipedia articles in different languages have been mined to support various tasks, such as Cross-Language Information Retrieval (CLIR) and Statistical Machine Translation (SMT). Articles on the same topic in different languages are often connected by inter-language links, which can be used to identify similar or comparable content. In this work, we investigate the correlation between similarity measures utilising language-independent and language-dependent features and respective human judgments. A collection of 800 Wikipedia pairs from 8 different language pairs were collected and judged for similarity by two assessors. We report the development of this corpus and inter-assessor agreement between judges across the languages. Results show that similarity measured using language independent features is comparable to using an approach based on translating non-English documents. In both cases the correlation with human judgments is low but also dependent upon the language pair. The results and corpus generated from this work also provide insights into the measurement of cross-language similarity.

Collecting and Using Comparable Corpora for Statistical Machine Translation
Inguna Skadiņa | Ahmet Aker | Nikos Mastropavlos | Fangzhong Su | Dan Tufis | Mateja Verlic | Andrejs Vasiļjevs | Bogdan Babych | Paul Clough | Robert Gaizauskas | Nikos Glaros | Monica Lestari Paramita | Mārcis Pinnis
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

Lack of sufficient parallel data for many languages and domains is currently one of the major obstacles to further advancement of automated translation. The ACCURAT project is addressing this issue by researching methods how to improve machine translation systems by using comparable corpora. In this paper we present tools and techniques developed in the ACCURAT project that allow additional data needed for statistical machine translation to be extracted from comparable corpora. We present methods and tools for acquisition of comparable corpora from the Web and other sources, for evaluation of the comparability of collected corpora, for multi-level alignment of comparable corpora and for extraction of lexical and terminological data for machine translation. Finally, we present initial evaluation results on the utility of collected corpora in domain-adapted machine translation and real-life applications.