Improving Unsegmented Dialogue Turns Annotation with N-gram Transducers

PACLIC 23 / City University of Hong Kong / 3-5 December 200

Active learning for dialogue act labelling

Active learning is a useful technique that allows for a considerably reduction of the amount of data we need to manually label in order to reach a good performance of a statistical model. In order to apply active learning to a particular task we need to previously define an effective selection criteria, that picks out the most informative samples at each iteration of active learning process. This is still an open problem that we are going to face in this work, in the task of dialogue annotation at dialogue act level. We present two different criteria, weighted number of hypothesis and entropy, that we have applied to the Sample Selection Algorithm for the task of dialogue act labelling, that retrieved appreciably improvements in our experimental approach. © 2011 Springer-Verlag.Work supported by the EC (FEDER/FSE) and the Spanish MEC/MICINN under the MIPRCV “Consolider Ingenio 2010” program (CSD2007-00018), MITTRAL (TIN2009-14633-C03-01) projects and the FPI scholarship (BES-2009-028965). Also supported by the Generalitat Valenciana under grant Prometeo/2009/014 and GV/2010/067Ghigi, F.; Tamarit Ballester, V.; Martínez-Hinarejos, C.; Benedí Ruiz, JM. (2011). Active learning for dialogue act labelling. En Lecture Notes in Computer Science. Springer Verlag (Germany). 6669:652-659. https://doi.org/10.1007/978-3-642-21257-4_81S6526596669Alcácer, N., Benedí, J.M., Blat, F., Granell, R., Martínez, C.D., Torres, F.: Acquisition and Labelling of a Spontaneous Speech Dialogue Corpus. In: SPECOM, Greece, pp. 583–586 (2005)Benedí, J.M., Lleida, E., Varona, A., Castro, M.J., Galiano, I., Justo, R., López, I., Miguel, A.: Design and acquisition of a telephone spontaneous speech dialogue corpus in spanish: DIHANA. In: Fifth LREC, Genova, Italy, pp. 1636–1639 (2006)Bunt, H.: Context and dialogue control. THINK Quarterly 3 (1994)Casacuberta, F., Vidal, E., Picó, D.: Inference of finite-state transducers from regular languages. Pat. Recognition 38(9), 1431–1443 (2005)Dybkjær, L., Minker, W. (eds.): Recent Trends in Discourse and Dialogue. Text, Speech and Language Technology, vol. 39. Springer, Dordrecht (2008)Gorin, A., Riccardi, G., Wright, J.: How may I help you? Speech Comm. 23, 113–127 (1997)Hwa, R.: Sample selection for statistical grammar induction. In: Proceedings of the 2000 Joint SIGDAT, pp. 45–52. Association for Computational Linguistics, Morristown (2000)Lavie, A., Levin, L., Zhan, P., Taboada, M., Gates, D., Lapata, M.M., Clark, C., Broadhead, M., Waibel, A.: Expanding the domain of a multi-lingual speech-to-speech translation system. In: Proceedings of the Workshop on Spoken Language Translation, ACL/EACL 1997 (1997)Martínez-Hinarejos, C.D., Tamarit, V., Benedí, J.M.: Improving unsegmented dialogue turns annotation with N-gram transducers. In: Proceedings of the 23rd Pacific Asia Conference on Language, Information and Computation (PACLIC23), vol. 1, pp. 345–354 (2009)Robinson, D.W.: Entropy and uncertainty, vol. 10, pp. 493–506 (2008)Stolcke, A., Coccaro, N., Bates, R., Taylor, P., van Ess-Dykema, C., Ries, K., Shriberg, E., Jurafsky, D., Martin, R., Meteer, M.: Dialogue act modelling for automatic tagging and recognition of conversational speech. Computational Linguistics 26(3), 1–34 (2000)Tamarit, V., Benedí, J., Martínez-Hinarejos, C.: Estimating the number of segments for improving dialogue act labelling. In: Proceedings of the First International Workshop of Spoken Dialog Systems Technology (2009)Young, S.: Probabilistic methods in spoken dialogue systems. Philosophical Trans. Royal Society (Series A) 358(1769), 1389–1402 (2000

Communicative Intentions Annotation Scheme for Natural Language Generation

Communicative intentions are one of the linguistic elements that usually determine the content of any text or message we want to express in our communicative interactions. With the purpose of contributing to the improvement of natural language generation systems, so that they can take the communicative intention as one of the starting points that will determine the structure and content of the message generated, the aim of this project is to create a communicative intentions annotation scheme based on the taxonomy presented in the Speech Act Theory. To do so, the scheme will be created with the help of a linguistic corpus and subsequently tested within a natural language generation system. In this way, it will be possible to check up to which point communicative intentions improve the planning stage of the text to be generated automatically, guiding the rest of decisions to be made by the system in order to create automatic messages with more similar results to any manually created text.This research work has been funded by the University of Alicante (Spain) and the Ministry of Economic Affairs and Digital Transformation of the Spanish Government through the project INTEGER (RTI2018-094649-B-I00)

Communicative Intentions Annotation Scheme for Natural Language Processing Applications

Communicative intentions are one of the linguistic elements that usually determine the content of any message we want to express in our social interactions. With the purpose of contributing to the improvement of natural language processing systems, this thesis aims to create a communicative intention annotation scheme based on the taxonomy presented in the Speech Act Theory. In this way, language processing tools could consider communicative intentions as a starting point to help classify any message and its content depending first on the intention it reflects. To do so, the scheme will be created with the help of an already annotated corpus of Spanish tweets and subsequently evaluated by external annotators so that we can confirm the appropriateness and reliability of the tagged intentions before applying the scheme to an NLP system. Thus, it will be possible to check up to which point communicative intentions can improve the identification of the purpose of a message in an already created NLP system so that we can gain more linguistic information from any text automatically.This research work is part of the R&D project "PID2021-123956OB-I00", funded by MCIN/AEI/10.13039/501100011033/ and by "ERDF A way of making Europe”. Moreover, it has been partially funded by the Generalitat Valenciana through the project NL4DISMIS: Natural Language Technologies for dealing with dis- and misinformation with grant reference (CIPROM/2021/21)"

Hierarchical Multi-Label Dialog Act Recognition on Spanish Data

Dialog acts reveal the intention behind the uttered words. Thus, their automatic recognition is important for a dialog system trying to understand its conversational partner. The study presented in this article approaches that task on the DIHANA corpus, whose three-level dialog act annotation scheme poses problems which have not been explored in recent studies. In addition to the hierarchical problem, the two lower levels pose multi-label classification problems. Furthermore, each level in the hierarchy refers to a different aspect concerning the intention of the speaker both in terms of the structure of the dialog and the task. Also, since its dialogs are in Spanish, it allows us to assess whether the state-of-the-art approaches on English data generalize to a different language. More specifically, we compare the performance of different segment representation approaches focusing on both sequences and patterns of words and assess the importance of the dialog history and the relations between the multiple levels of the hierarchy. Concerning the single-label classification problem posed by the top level, we show that the conclusions drawn on English data also hold on Spanish data. Furthermore, we show that the approaches can be adapted to multi-label scenarios. Finally, by hierarchically combining the best classifiers for each level, we achieve the best results reported for this corpus.Comment: 21 pages, 4 figures, 17 tables, translated version of the article published in Linguam\'atica 11(1

Improving the automatic segmentation of subtitles through conditional random field

[EN] Automatic segmentation of subtitles is a novel research field which has not been studied extensively to date. However, quality automatic subtitling is a real need for broadcasters which seek for automatic solutions given the demanding European audiovisual legislation. In this article, a method based on Conditional Random Field is presented to deal with the automatic subtitling segmentation. This is a continuation of a previous work in the field, which proposed a method based on Support Vector Machine classifier to generate possible candidates for breaks. For this study, two corpora in Basque and Spanish were used for experiments, and the performance of the current method was tested and compared with the previous solution and two rule-based systems through several evaluation metrics. Finally, an experiment with human evaluators was carried out with the aim of measuring the productivity gain in post-editing automatic subtitles generated with the new method presented.This work was partially supported by the project CoMUN-HaT - TIN2015-70924-C2-1-R (MINECO/FEDER).Alvarez, A.; Martínez-Hinarejos, C.; Arzelus, H.; Balenciaga, M.; Del Pozo, A. (2017). Improving the automatic segmentation of subtitles through conditional random field. Speech Communication. 88:83-95. https://doi.org/10.1016/j.specom.2017.01.010S83958

Joint models for concept-to-text generation

Much of the data found on the world wide web is in numeric, tabular, or other nontextual format (e.g., weather forecast tables, stock market charts, live sensor feeds), and thus inaccessible to non-experts or laypersons. However, most conventional search engines and natural language processing tools (e.g., summarisers) can only handle textual input. As a result, data in non-textual form remains largely inaccessible. Concept-to- text generation refers to the task of automatically producing textual output from non-linguistic input, and holds promise for rendering non-linguistic data widely accessible. Several successful generation systems have been produced in the past twenty years. They mostly rely on human-crafted rules or expert-driven grammars, implement a pipeline architecture, and usually operate in a single domain. In this thesis, we present several novel statistical models that take as input a set of database records and generate a description of them in natural language text. Our unique idea is to combine the processes of structuring a document (document planning), deciding what to say (content selection) and choosing the specific words and syntactic constructs specifying how to say it (lexicalisation and surface realisation), in a uniform joint manner. Rather than breaking up the generation process into a sequence of local decisions, we define a probabilistic context-free grammar that globally describes the inherent structure of the input (a corpus of database records and text describing some of them). This joint representation allows individual processes (i.e., document planning, content selection, and surface realisation) to communicate and influence each other naturally. We recast generation as the task of finding the best derivation tree for a set of input database records and our grammar, and describe several algorithms for decoding in this framework that allows to intersect the grammar with additional information capturing fluency and syntactic well-formedness constraints. We implement our generators using the hypergraph framework. Contrary to traditional systems, we learn all the necessary document, structural and linguistic knowledge from unannotated data. Additionally, we explore a discriminative reranking approach on the hypergraph representation of our model, by including more refined content selection features. Central to our approach is the idea of porting our models to various domains; we experimented on four widely different domains, namely sportscasting, weather forecast generation, booking flights, and troubleshooting guides. The performance of our systems is competitive and often superior compared to state-of-the-art systems that use domain specific constraints, explicit feature engineering or labelled data

Speech technologies for the audiovisual and multimedia interaction environments

361 p