| 1. |
- Smith, Cameron, et al.
(författare)
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Interaction strategies for an affective conversational agent
- 2010
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Ingår i: Proceedings of the 10th International Conference on Intelligent Virtual Agents (IVA 2010), Lecture Notes in Computer Science book series (LNCS, volume 6356). - Berlin and Heidelberg : Springer Verlag. - 0302-9743 .- 1611-3349. - 9783642158919 - 3642158919
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Konferensbidrag (refereegranskat)abstract
- The development of Embodied Conversational Agents (ECA) as Companions brings several challenges for both affective and conversational dialogue. These include challenges in generating appropriate affective responses, selecting the overall shape of the dialogue, providing prompt system response times and handling interruptions. We present an implementation of such a Companion showing the development of individual modules that attempt to address these challenges. Further, to resolve resulting conflicts, we present encompassing interaction strategies that attempt to balance the competing requirements. Finally, we present dialogues from our working prototype to illustrate these interaction strategies in operation.
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| 2. |
- Volodina, Elena, 1973, et al.
(författare)
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Grandma Karl is 27 years old – research agenda for pseudonymization of research data
- 2023
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Ingår i: 2023 IEEE Ninth International Conference on Big Data Computing Service and Applications (BigDataService), Athens, Greece, 2023. - Los Alamitos : IEEE Computer Society. - 9798350333794 - 9798350335347
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Konferensbidrag (refereegranskat)abstract
- Accessibility of research data is critical for advances in many research fields, but textual data often cannot be shared due to the personal and sensitive information which it con- tains, e.g names or political opinions. General Data Protection Regulation (GDPR) suggests pseudonymization as a solution to secure open access to research data, but we need to learn more about pseudonymization as an approach before adopting it for manipulation of research data. This paper outlines a research agenda within pseudonymization, namely need of studies into the effects of pseudonymization on unstructured data in relation to e.g. readability and language assessment, as well as the effectiveness of pseudonymization as a way of protecting writer identity, while also exploring different ways of developing context-sensitive algorithms for detection, labelling and replacement of personal information in unstructured data. The recently granted project on pseudonymization ‘Grandma Karl is 27 years old’1 addresses exactly those challenges.
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| 3. |
- Adouane, Wafia, 1985, et al.
(författare)
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A Comparison of Character Neural Language Model and Bootstrapping for Language Identification in Multilingual Noisy Texts
- 2018
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Ingår i: Proceedings of the Second Workshop on Subword and Character Level Models in NLP (SCLeM), June 6, 2018 New Orleans, Louisiana. - New Orleans, Louisiana USA. - 9781948087186
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Konferensbidrag (refereegranskat)abstract
- This paper seeks to examine the effect of including background knowledge in the form of character pre-trained neural language model (LM), and data bootstrapping to overcome the problem of unbalanced limited resources. As a test, we explore the task of language identification in mixed-language short non-edited texts with an under-resourced language, namely the case of Algerian Arabic for which both labelled and unlabelled data are limited. We compare the performance of two traditional machine learning methods and a deep neural networks (DNNs) model. The results show that overall DNNs perform better on labelled data for the majority categories and struggle with the minority ones. While the effect of the untokenised and unlabelled data encoded as LM differs for each category, bootstrapping, however, improves the performance of all systems and all categories. These methods are language independent and could be generalised to other under-resourced languages for which a small labelled data and a larger unlabelled data are available.
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| 4. |
- Adouane, Wafia, 1985, et al.
(författare)
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Identification of Languages in Algerian Arabic Multilingual Documents
- 2017
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Ingår i: Proceedings of The Third Arabic Natural Language Processing Workshop (WANLP), Valencia, Spain, April 3, 2017. - Valencia, Spain : Association for Computational Linguistics. - 9781945626449
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Konferensbidrag (refereegranskat)abstract
- This paper presents a language identification system designed to detect the language of each word, in its context, in a multilingual documents as generated in social media by bilingual/multilingual communities, in our case speakers of Algerian Arabic. We frame the task as a sequence tagging problem and use supervised machine learning with standard methods like HMM and Ngram classifi- cation tagging. We also experiment with a lexicon-based method. Combining all the methods in a fall-back mechanism and introducing some linguistic rules, to deal with unseen tokens and ambiguous words, gives an overall accuracy of 93.14%. Finally, we introduced rules for language identification from sequences of recognised words.
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| 5. |
- Adouane, Wafia, 1985, et al.
(författare)
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Improving Neural Network Performance by Injecting Background Knowledge: Detecting Code-switching and Borrowing in Algerian texts
- 2018
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Ingår i: Proceedings of the Third Workshop on Computational Approaches to Linguistic Code-switching, Melbourne, Australia, July 19, 2018. - Melbourne, Australia : Association for Computational Linguistics. - 9781948087452
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Konferensbidrag (refereegranskat)abstract
- We explore the effect of injecting back- ground knowledge to different deep neural network (DNN) configurations in order to mitigate the problem of the scarcity of annotated data when applying these models on datasets of low-resourced languages. The background knowledge is encoded in the form of lexicons and pre-trained sub-word embeddings. The DNN models are evaluated on the task of detecting code-switching and borrowing points in non-standardised user-generated Algerian texts. Overall results show that DNNs benefit from adding background knowledge. However, the gain varies between models and categories. The proposed DNN architectures are generic and could be applied to other low-resourced languages.
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| 6. |
- Adouane, Wafia, 1985, et al.
(författare)
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Neural Models for Detecting Binary Semantic Textual Similarity for Algerian and MSA
- 2019
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Ingår i: Proceedings of the Fourth Arabic Natural Language Processing Workshop, WANLP 2019, Jul 28-Aug 2, Florence, Italy. pp. 78-87. - Florence, Italy : Association for Computational Linguistics. - 9781950737321
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Konferensbidrag (refereegranskat)abstract
- We explore the extent to which neural networks can learn to identify semantically equivalent sentences from a small variable dataset using an end-to-end training. We collect a new noisy non-standardised user-generated Algerian (ALG) dataset and also translate it to Modern Standard Arabic (MSA) which serves as its regularised counterpart. We compare the performance of various models on both datasets and report the best performing configurations. The results show that relatively simple models composed of 2 LSTM layers outperform by far other more sophisticated attention-based architectures, for both ALG and MSA datasets.
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| 7. |
- Adouane, Wafia, 1985, et al.
(författare)
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Normalising Non-standardised Orthography in Algerian Code-switched User-generated Data
- 2019
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Ingår i: The 5th Workshop on Noisy User-generated Text (W-NUT), November 4, 2019, Hong Kong / Wei Xu, Alan Ritter, Tim Baldwin, Afshin Rahimi (Editors). - Stroudsburg, PA : Association for Computational Linguistics. - 9781950737840
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Konferensbidrag (refereegranskat)abstract
- We work with Algerian, an under-resourced non-standardised Arabic variety, for which we compile a new parallel corpus consist- ing of user-generated textual data matched with normalised and corrected human annota- tions following data-driven and our linguisti- cally motivated standard. We use an end-to- end deep neural model designed to deal with context-dependent spelling correction and nor- malisation. Results indicate that a model with two CNN sub-network encoders and an LSTM decoder performs the best, and that word context matters. Additionally, pre- processing data token-by-token with an edit- distance based aligner significantly improves the performance. We get promising results for the spelling correction and normalisation, as a pre-processing step for downstream tasks, on detecting binary Semantic Textual Similarity.
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| 8. |
- Amanaki, Erini, et al.
(författare)
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Fine-grained Entailment: Resources for Greek NLI and Precise Entailment
- 2022
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Ingår i: Proceedings of the Workshop on Dataset Creation for Lower-Resourced Languages within the 13th Language Resources and Evaluation Conference. - Marseille, France : European Language Resources Association (ELRA). - 9782493814067
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Konferensbidrag (refereegranskat)abstract
- In this paper, we present a number of fine-grained resources for Natural Language Inference (NLI). In particular, we present a number of resources and validation methods for Greek NLI and a resource for precise NLI. First, we extend the Greek version of the FraCaS test suite to include examples where the inference is directly linked to the syntactic/morphological properties of Greek. The new resource contains an additional 428 examples, making it in total a dataset of 774 examples. Expert annotators have been used in order to create the additional resource, while extensive validation of the original Greek version of the FraCaS by non-expert and expert subjects is performed. Next, we continue the work initiated by (CITATION), according to which a subset of the RTE problems have been labeled for missing hypotheses and we present a dataset an order of magnitude larger, annotating the whole SuperGlUE/RTE dataset with missing hypotheses. Lastly, we provide a de-dropped version of the Greek XNLI dataset, where the pronouns that are missing due to the pro-drop nature of the language are inserted. We then run some models to see the effect of that insertion and report the results.
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| 9. |
- Bizzoni, Yuri, 1989, et al.
(författare)
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Distributional semantic models for detection of textual entailment
- 2016
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Ingår i: The Sixth Swedish language technology conference (SLTC), Umeå University, 17-18 November, 2016, (ed. Björklund, Johanna and Stymne, Sara). - Umeå : Umeå University.
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Konferensbidrag (refereegranskat)abstract
- We present our experiments on integrating and evaluating distributional semantics with the recognising textual entailment task (RTE). We consider entailment as semantic similarity between text and hypothesis coupled with additional heuristic, which can be either selecting the top scoring hypothesis or a pre-defined threshold. We show that a distributional model is particularly good at detecting entailment related to “world knowledge”, and that aligning the hypothesis with the text improves detection of lexical entailment.
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| 10. |
- Bizzoni, Yuri, 1989, et al.
(författare)
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Sky + fire = sunset. Exploring parallels between visually grounded metaphors and image classifiers
- 2020
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Ingår i: Proceedings of the Second Workshop on Figurative Language Processing (FLP) at ACL-2020, July 9, 2020 / Beata Beigman Klebanov, Ekaterina Shutova, Patricia Lichtenstein, Smaranda Muresan, Chee Wee, Anna Feldman, Debanjan Ghosh (Editors). - Stroudsburg, PA, USA : Association for Computational Linguistics. - 9781952148125
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Konferensbidrag (refereegranskat)abstract
- This work explores the differences and similarities between neural image classifiers' mis-categorisations and visually grounded metaphors - that we could conceive as intentional mis-categorisations. We discuss the possibility of using automatic image classifiers to approximate human metaphoric behaviours, and the limitations of such frame. We report two pilot experiments to study grounded metaphoricity. In the first we represent metaphors as a form of visual mis-categorisation. In the second we model metaphors as a more flexible, compositional operation in a continuous visual space generated from automatic classification systems.
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