A Train-on-Target Strategy for Multilingual Spoken Language Understanding

[EN] There are two main strategies to adapt a Spoken Language Understanding system to deal with languages different from the original (source) language: test-on-source and train-on-target. In the train-ontarget approach, a new understanding model is trained in the target language, which is the language in which the test utterances are pronounced. To do this, a segmented and semantically labeled training set for each new language is needed. In this work, we use several general-purpose translators to obtain the translation of the training set and we apply an alignment process to automatically segment the training sentences. We have applied this train-on-target approach to estimate the understanding module of a Spoken Dialog System for the DIHANA task, which consists of an information system about train timetables and fares in Spanish. We present an evaluation of our train-on-target multilingual approach for two target languages, French and EnglishThis work has been partially funded by the project ASLP-MULAN: Audio, Speech and Language Processing for Multimedia Analytics (MEC TIN2014-54288-C4-3-R).García-Granada, F.; Segarra Soriano, E.; Millán, C.; Sanchís Arnal, E.; Hurtado Oliver, LF. (2016). A Train-on-Target Strategy for Multilingual Spoken Language Understanding. Lecture Notes in Computer Science. 10077:224-233. https://doi.org/10.1007/978-3-319-49169-1_22S22423310077Benedí, J.M., Lleida, E., Varona, A., Castro, M.J., Galiano, I., Justo, R., López de Letona, I., Miguel, A.: Design and acquisition of a telephone spontaneous speech dialogue corpus in Spanish: DIHANA. In: LREC 2006, pp. 1636–1639 (2006)Calvo, M., Hurtado, L.-F., García, F., Sanchís, E.: A Multilingual SLU system based on semantic decoding of graphs of words. In: Torre Toledano, D., Ortega Giménez, A., Teixeira, A., González Rodríguez, J., Hernández Gómez, L., San Segundo Hernández, R., Ramos Castro, D. (eds.) IberSPEECH 2012. CCIS, vol. 328, pp. 158–167. Springer, Heidelberg (2012). doi: 10.1007/978-3-642-35292-8_17Calvo, M., Hurtado, L.F., Garca, F., Sanchis, E., Segarra, E.: Multilingual spoken language understanding using graphs and multiple translations. Comput. Speech Lang. 38, 86–103 (2016)Dinarelli, M., Moschitti, A., Riccardi, G.: Concept segmentation and labeling for conversational speech. In: Interspeech, Brighton, UK (2009)Esteve, Y., Raymond, C., Bechet, F., Mori, R.D.: Conceptual decoding for spoken dialog systems. In: Proceedings of EuroSpeech 2003, pp. 617–620 (2003)García, F., Hurtado, L., Segarra, E., Sanchis, E., Riccardi, G.: Combining multiple translation systems for spoken language understanding portability. In: Proceedings of IEEE Workshop on Spoken Language Technology (SLT), pp. 282–289 (2012)Hahn, S., Dinarelli, M., Raymond, C., Lefèvre, F., Lehnen, P., De Mori, R., Moschitti, A., Ney, H., Riccardi, G.: Comparing stochastic approaches to spoken language understanding in multiple languages. IEEE Trans. Audio Speech Lang. Process. 6(99), 1569–1583 (2010)He, Y., Young, S.: A data-driven spoken language understanding system. In: Proceedings of ASRU 2003, pp. 583–588 (2003)Hurtado, L., Segarra, E., García, F., Sanchis, E.: Language understanding using n-multigram models. In: Vicedo, J.L., Martínez-Barco, P., Muńoz, R., Saiz Noeda, M. (eds.) EsTAL 2004. LNCS (LNAI), vol. 3230, pp. 207–219. Springer, Heidelberg (2004). doi: 10.1007/978-3-540-30228-5_19Jabaian, B., Besacier, L., Lefèvre, F.: Comparison and combination of lightly supervised approaches for language portability of a spoken language understanding system. IEEE Trans. Audio Speech Lang. Process. 21(3), 636–648 (2013)Koehn, P., et al.: Moses: open source toolkit for statistical machine translation. In: Proceedings of ACL Demonstration Session, pp. 177–180 (2007)Lafferty, J., McCallum, A., Pereira, F.: Conditional random fields: probabilistic models for segmenting and labeling sequence data. In: International Conference on Machine Learning, pp. 282–289. Citeseer (2001)Lefèvre, F.: Dynamic Bayesian networks and discriminative classifiers for multi-stage semantic interpretation. In: IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2007, vol. 4, pp. 13–16. IEEE (2007)Ortega, L., Galiano, I., Hurtado, L.F., Sanchis, E., Segarra, E.: A statistical segment-based approach for spoken language understanding. In: Proceedings of InterSpeech 2010, Makuhari, Chiba, Japan, pp. 1836–1839 (2010)Segarra, E., Sanchis, E., Galiano, M., García, F., Hurtado, L.: Extracting semantic information through automatic learning techniques. IJPRAI 16(3), 301–307 (2002)Servan, C., Camelin, N., Raymond, C., Bchet, F., Mori, R.D.: On the use of machine translation for spoken language understanding portability. In: Proceedings of ICASSP 2010, pp. 5330–5333 (2010)Tür, G., Mori, R.D.: Spoken Language Understanding: Systems for Extracting Semantic Information from Speech, 1st edn. Wiley, Hoboken (2011

Metaphoric competence and communicative language ability

Recent developments in cognitive linguistics have highlighted the importance as well as the ubiquity of metaphor in language. Despite this, the ability of second language learners to use metaphors is often still not seen as a core ability. In this paper, we take a model of communicative competence that has been widely influential in both language teaching and language testing, namely Bachman (1990), and argue, giving a range of examples of language use and learner difficulty, that metaphoric competence has in fact an important role to play in all areas of communicative competence. In other words, it can contribute centrally to grammatical competence, textual competence, illocutionary competence, sociolinguistic competence, and strategic competence. Metaphor is thus highly relevant to second language learning, teaching and testing, from the earliest to the most advanced stages of learning

Directional adposition use in English, Swedish and Finnish

Directional adpositions such as to the left of describe where a Figure is in relation to a Ground. English and Swedish directional adpositions refer to the location of a Figure in relation to a Ground, whether both are static or in motion. In contrast, the Finnish directional adpositions edellä (in front of) and jäljessä (behind) solely describe the location of a moving Figure in relation to a moving Ground (Nikanne, 2003). When using directional adpositions, a frame of reference must be assumed for interpreting the meaning of directional adpositions. For example, the meaning of to the left of in English can be based on a relative (speaker or listener based) reference frame or an intrinsic (object based) reference frame (Levinson, 1996). When a Figure and a Ground are both in motion, it is possible for a Figure to be described as being behind or in front of the Ground, even if neither have intrinsic features. As shown by Walker (in preparation), there are good reasons to assume that in the latter case a motion based reference frame is involved. This means that if Finnish speakers would use edellä (in front of) and jäljessä (behind) more frequently in situations where both the Figure and Ground are in motion, a difference in reference frame use between Finnish on one hand and English and Swedish on the other could be expected. We asked native English, Swedish and Finnish speakers’ to select adpositions from a language specific list to describe the location of a Figure relative to a Ground when both were shown to be moving on a computer screen. We were interested in any differences between Finnish, English and Swedish speakers. All languages showed a predominant use of directional spatial adpositions referring to the lexical concepts TO THE LEFT OF, TO THE RIGHT OF, ABOVE and BELOW. There were no differences between the languages in directional adpositions use or reference frame use, including reference frame use based on motion. We conclude that despite differences in the grammars of the languages involved, and potential differences in reference frame system use, the three languages investigated encode Figure location in relation to Ground location in a similar way when both are in motion. Levinson, S. C. (1996). Frames of reference and Molyneux’s question: Crosslingiuistic evidence. In P. Bloom, M.A. Peterson, L. Nadel & M.F. Garrett (Eds.) Language and Space (pp.109-170). Massachusetts: MIT Press. Nikanne, U. (2003). How Finnish postpositions see the axis system. In E. van der Zee & J. Slack (Eds.), Representing direction in language and space. Oxford, UK: Oxford University Press. Walker, C. (in preparation). Motion encoding in language, the use of spatial locatives in a motion context. Unpublished doctoral dissertation, University of Lincoln, Lincoln. United Kingdo

Visual world studies of conversational perspective taking: similar findings, diverging interpretations

Visual-world eyetracking greatly expanded the potential for insight into how listeners access and use common ground during situated language comprehension. Past reviews of visual world studies on perspective taking have largely taken the diverging findings of the various studies at face value, and attributed these apparently different findings to differences in the extent to which the paradigms used by different labs afford collaborative interaction. Researchers are asking questions about perspective taking of an increasingly nuanced and sophisticated nature, a clear indicator of progress. But this research has the potential not only to improve our understanding of conversational perspective taking. Grappling with problems of data interpretation in such a complex domain has the unique potential to drive visual world researchers to a deeper understanding of how to best map visual world data onto psycholinguistic theory. I will argue against this interactional affordances explanation, on two counts. First, it implies that interactivity affects the overall ability to form common ground, and thus provides no straightforward explanation of why, within a single noninteractive study, common ground can have very large effects on some aspects of processing (referential anticipation) while having negligible effects on others (lexical processing). Second, and more importantly, the explanation accepts the divergence in published findings at face value. However, a closer look at several key studies shows that the divergences are more likely to reflect inconsistent practices of analysis and interpretation that have been applied to an underlying body of data that is, in fact, surprisingly consistent. The diverging interpretations, I will argue, are the result of differences in the handling of anticipatory baseline effects (ABEs) in the analysis of visual world data. ABEs arise in perspective-taking studies because listeners have earlier access to constraining information about who knows what than they have to referential speech, and thus can already show biases in visual attention even before the processing of any referential speech has begun. To be sure, these ABEs clearly indicate early access to common ground; however, access does not imply integration, since it is possible that this information is not used later to modulate the processing of incoming speech. Failing to account for these biases using statistical or experimental controls leads to over-optimistic assessments of listeners’ ability to integrate this information with incoming speech. I will show that several key studies with varying degrees of interactional affordances all show similar temporal profiles of common ground use during the interpretive process: early anticipatory effects, followed by bottom-up effects of lexical processing that are not modulated by common ground, followed (optionally) by further late effects that are likely to be post-lexical. Furthermore, this temporal profile for common ground radically differs from the profile of contextual effects related to verb semantics. Together, these findings are consistent with the proposal that lexical processes are encapsulated from common ground, but cannot be straightforwardly accounted for by probabilistic constraint-based approaches

Do (and say) as I say: Linguistic adaptation in human-computer dialogs

© Theodora Koulouri, Stanislao Lauria, and Robert D. Macredie. This article has been made available through the Brunel Open Access Publishing Fund.There is strong research evidence showing that people naturally align to each other’s vocabulary, sentence structure, and acoustic features in dialog, yet little is known about how the alignment mechanism operates in the interaction between users and computer systems let alone how it may be exploited to improve the efficiency of the interaction. This article provides an account of lexical alignment in human–computer dialogs, based on empirical data collected in a simulated human–computer interaction scenario. The results indicate that alignment is present, resulting in the gradual reduction and stabilization of the vocabulary-in-use, and that it is also reciprocal. Further, the results suggest that when system and user errors occur, the development of alignment is temporarily disrupted and users tend to introduce novel words to the dialog. The results also indicate that alignment in human–computer interaction may have a strong strategic component and is used as a resource to compensate for less optimal (visually impoverished) interaction conditions. Moreover, lower alignment is associated with less successful interaction, as measured by user perceptions. The article distills the results of the study into design recommendations for human–computer dialog systems and uses them to outline a model of dialog management that supports and exploits alignment through mechanisms for in-use adaptation of the system’s grammar and lexicon

The role of language in mathematical development: Evidence from children with specific language impairments

A sample (n=48) of eight year olds with Specific Language Impairments is compared with age-matched (n=55) and language matched controls (n=55) on a range of tasks designed to test the interdependence of language and mathematical development. Performance across tasks varies substantially in the SLI group, showing profound deficits in production of the count word sequence and basic calculation and significant deficits in understanding of the place-value principle in Hindu-Arabic notation. Only in understanding of arithmetic principles does SLI performance approximate that of age-matched-controls, indicating that principled understanding can develop even where number sequence production and other aspects of number processing are severely compromised

Towards Zero-Shot Frame Semantic Parsing for Domain Scaling

State-of-the-art slot filling models for goal-oriented human/machine conversational language understanding systems rely on deep learning methods. While multi-task training of such models alleviates the need for large in-domain annotated datasets, bootstrapping a semantic parsing model for a new domain using only the semantic frame, such as the back-end API or knowledge graph schema, is still one of the holy grail tasks of language understanding for dialogue systems. This paper proposes a deep learning based approach that can utilize only the slot description in context without the need for any labeled or unlabeled in-domain examples, to quickly bootstrap a new domain. The main idea of this paper is to leverage the encoding of the slot names and descriptions within a multi-task deep learned slot filling model, to implicitly align slots across domains. The proposed approach is promising for solving the domain scaling problem and eliminating the need for any manually annotated data or explicit schema alignment. Furthermore, our experiments on multiple domains show that this approach results in significantly better slot-filling performance when compared to using only in-domain data, especially in the low data regime.Comment: 4 pages + 1 reference

A taxonomy for interactive educational multimedia

Learning is more than knowledge acquisition; it often involves the active participation of the learner in a variety of knowledge- and skills-based learning and training activities. Interactive multimedia technology can support the variety of interaction channels and languages required to facilitate interactive learning and teaching. We will present a taxonomy for interactive educational multimedia that supports the classification, description and development of such systems. Such a taxonomy needs to embed multimedia technology into a coherent educational context. A conceptual framework based on an integrated interaction model is needed to capture learning and training activities in an online setting from an educational perspective, describe them in the human-computer context, and integrate them with mechanisms and principles of multimedia interaction