A multilingual neural coaching model with enhanced long-term dialogue structure

In this work we develop a fully data-driven conversational agent capable of carrying out motivational coach- ing sessions in Spanish, French, Norwegian, and English. Unlike the majority of coaching, and in general well-being related conversational agents that can be found in the literature, ours is not designed by hand- crafted rules. Instead, we directly model the coaching strategy of professionals with end users. To this end, we gather a set of virtual coaching sessions through a Wizard of Oz platform, and apply state of the art Natural Language Processing techniques. We employ a transfer learning approach, pretraining GPT2 neural language models and fine-tuning them on our corpus. However, since these only take as input a local dialogue history, a simple fine-tuning procedure is not capable of modeling the long-term dialogue strategies that appear in coaching sessions. To alleviate this issue, we first propose to learn dialogue phase and scenario embeddings in the fine-tuning stage. These indicate to the model at which part of the dialogue it is and which kind of coaching session it is carrying out. Second, we develop a global deep learning system which controls the long-term structure of the dialogue. We also show that this global module can be used to visualize and interpret the decisions taken by the the conversational agent, and that the learnt representations are comparable to dialogue acts. Automatic and human evaluation show that our proposals serve to improve the baseline models. Finally, interaction experiments with coaching experts indicate that the system is usable and gives rise to positive emotions in Spanish, French and English, while the results in Norwegian point out that there is still work to be done in fully data driven approaches with very low resource languages.This work has been partially funded by the Basque Government under grant PRE_2017_1_0357 and by the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 769872

Bringing Statistical Methodologies for Enterprise Integration of Conversational Agents

Proceedings of: 9th International Conference on Practical Applications of Agents and Multiagent Systems (PAAMS 11). Salamanca, 6-8 April, 2011In this paper we present a methodology to develop commercial conversational agents that avoids the effort of manually defining the dialog strategy for the dialog management module. Our corpus-based methodology is based on selecting the next system answer by means of a classification process in which the complete dialog history is considered. This way, system developers can employ standards like VoiceXML to simply define system prompts and the associated grammars to recognize the users responses to the prompt, and the statistical dialog model automatically selects the next system prompt.We have applied this methodology for the development of an academic conversational agent.Funded by projects CICYT TIN2008-06742-C02-02/TSI, CICYT TEC 2008-06732-C02-02/TEC, CAM CONTEXTS (S2009/TIC-1485), and DPS2008-07029- C02-02.Publicad

Proceedings of the COLING 2004 Post Conference Workshop on Multilingual Linguistic Ressources MLR2004

International audienceIn an ever expanding information society, most information systems are now facing the "multilingual challenge". Multilingual language resources play an essential role in modern information systems. Such resources need to provide information on many languages in a common framework and should be (re)usable in many applications (for automatic or human use). Many centres have been involved in national and international projects dedicated to building har- monised language resources and creating expertise in the maintenance and further development of standardised linguistic data. These resources include dictionaries, lexicons, thesauri, word-nets, and annotated corpora developed along the lines of best practices and recommendations. However, since the late 90's, most efforts in scaling up these resources remain the responsibility of the local authorities, usually, with very low funding (if any) and few opportunities for academic recognition of this work. Hence, it is not surprising that many of the resource holders and developers have become reluctant to give free access to the latest versions of their resources, and their actual status is therefore currently rather unclear. The goal of this workshop is to study problems involved in the development, management and reuse of lexical resources in a multilingual context. Moreover, this workshop provides a forum for reviewing the present state of language resources. The workshop is meant to bring to the international community qualitative and quantitative information about the most recent developments in the area of linguistic resources and their use in applications. The impressive number of submissions (38) to this workshop and in other workshops and conferences dedicated to similar topics proves that dealing with multilingual linguistic ressources has become a very hot problem in the Natural Language Processing community. To cope with the number of submissions, the workshop organising committee decided to accept 16 papers from 10 countries based on the reviewers' recommendations. Six of these papers will be presented in a poster session. The papers constitute a representative selection of current trends in research on Multilingual Language Resources, such as multilingual aligned corpora, bilingual and multilingual lexicons, and multilingual speech resources. The papers also represent a characteristic set of approaches to the development of multilingual language resources, such as automatic extraction of information from corpora, combination and re-use of existing resources, online collaborative development of multilingual lexicons, and use of the Web as a multilingual language resource. The development and management of multilingual language resources is a long-term activity in which collaboration among researchers is essential. We hope that this workshop will gather many researchers involved in such developments and will give them the opportunity to discuss, exchange, compare their approaches and strengthen their collaborations in the field. The organisation of this workshop would have been impossible without the hard work of the program committee who managed to provide accurate reviews on time, on a rather tight schedule. We would also like to thank the Coling 2004 organising committee that made this workshop possible. Finally, we hope that this workshop will yield fruitful results for all participants

Automating the anonymisation of textual corpora

[EU] Gaur egun, testu berriak etengabe sortzen doaz sare sozialetako mezu, osasun-txosten, dokumentu o zial eta halakoen ondorioz. Hala ere, testuok informazio pertsonala baldin badute, ezin dira ikerkuntzarako edota beste helburutarako baliatu, baldin eta aldez aurretik ez badira anonimizatzen. Anonimizatze hori automatikoki egitea erronka handia da eta askotan hutsetik anotatutako domeinukako datuak behar dira, ez baita arrunta helburutzat ditugun testuinguruetarako anotatutako corpusak izatea. Hala, tesi honek bi helburu ditu: (i) Gaztelaniazko elkarrizketa espontaneoz osatutako corpus anonimizatu berri bat konpilatu eta eskura jartzea, eta (ii) sortutako baliabide hau ustiatzea informazio sentiberaren identi kazio-teknikak aztertzeko, helburu gisa dugun domeinuan testu etiketaturik izan gabe. Hala, lehenengo helburuari lotuta, ES-Port izeneko corpusa sortu dugu. Telekomunikazio-ekoizle batek ahoz laguntza teknikoa ematen duenean sortu diren 1170 elkarrizketa espontaneoek osatzen dute corpusa. Ordezkatze-tekniken bidez anonimizatu da, eta ondorioz emaitza testu irakurgarri eta naturala izan da. Hamaika anonimizazio-kategoria landu dira, eta baita hizkuntzakoak eta hizkuntzatik kanpokoak diren beste zenbait anonimizazio-fenomeno ere, hala nola, kode-aldaketa, barrea, errepikapena, ahoskatze okerrak, eta abar. Bigarren helburuari lotuta, berriz, anonimizatu beharreko informazio sentibera identi katzeko, gordailuan oinarritutako Ikasketa Aktiboa erabili da, honek helburutzat baitu ahalik eta testu anotatu gutxienarekin sailkatzaile ahalik eta onena lortzea. Horretaz gain, emaitzak hobetzeko, eta abiapuntuko hautaketarako eta galderen hautaketarako estrategiak aztertzeko, Ezagutza Transferentzian oinarritutako teknikak ustiatu dira, aldez aurretik anotatuta zegoen corpus txiki bat oinarri hartuta. Emaitzek adierazi dute, lan honetan aukeratutako metodoak egokienak izan direla abiapuntuko hautaketa egiteko eta kontsulta-estrategia gisa iturri eta helburu sailkapenen zalantzak konbinatzeak Ikasketa Aktiboa hobetzen duela, ikaskuntza-kurba bizkorragoak eta sailkapen-errendimendu handiagoak lortuz iterazio gutxiagotan.[EN] A huge amount of new textual data are created day by day through social media posts, health records, official documents, and so on. However, if such resources contain personal data, they cannot be shared for research or other purposes without undergoing proper anonymisation. Automating such task is challenging and often requires labelling in-domain data from scratch since anonymised annotated corpora for the target scenarios are rarely available. This thesis has two main objectives: (i) to compile and provide a new corpus in Spanish with annotated anonymised spontaneous dialogue data, and (ii) to exploit the newly provided resource to investigate techniques for automating the sensitive data identification task, in a setting where initially no annotated data from the target domain are available. Following such aims, first, the ES-Port corpus is presented. It is a compilation of 1170 spontaneous spoken human-human dialogues from calls to the technical support service of a telecommunications provider. The corpus has been anonymised using the substitution technique, which implies the result is a readable natural text, and it contains annotations of eleven different anonymisation categories, as well as some linguistic and extra-linguistic phenomena annotations like code-switching, laughter, repetitions, mispronunciations, and so on. Next, the compiled corpus is used to investigate automatic sensitive data identification within a pool-based Active Learning framework, whose aim is to obtain the best possible classifier having to annotate as little data as possible. In order to improve such setting, Knowledge Transfer techniques from another small available anonymisation annotated corpus are explored for seed selection and query selection strategies. Results show that using the proposed seed selection methods obtain the best seeds on which to initialise the base learner's training and that combining source and target classifiers' uncertainties as query strategy improves the Active Learning process, deriving in steeper learning curves and reaching top classifier performance in fewer iterations

The timing bottleneck: Why timing and overlap are mission-critical for conversational user interfaces, speech recognition and dialogue systems

Speech recognition systems are a key intermediary in voice-driven human-computer interaction. Although speech recognition works well for pristine monologic audio, real-life use cases in open-ended interactive settings still present many challenges. We argue that timing is mission-critical for dialogue systems, and evaluate 5 major commercial ASR systems for their conversational and multilingual support. We find that word error rates for natural conversational data in 6 languages remain abysmal, and that overlap remains a key challenge (study 1). This impacts especially the recognition of conversational words (study 2), and in turn has dire consequences for downstream intent recognition (study 3). Our findings help to evaluate the current state of conversational ASR, contribute towards multidimensional error analysis and evaluation, and identify phenomena that need most attention on the way to build robust interactive speech technologies

On the Development of Adaptive and User-Centred Interactive Multimodal Interfaces

Multimodal systems have attained increased attention in recent years, which has made possible important improvements in the technologies for recognition, processing, and generation of multimodal information. However, there are still many issues related to multimodality which are not clear, for example, the principles that make it possible to resemble human-human multimodal communication. This chapter focuses on some of the most important challenges that researchers have recently envisioned for future multimodal interfaces. It also describes current efforts to develop intelligent, adaptive, proactive, portable and affective multimodal interfaces

Statistically motivated example-based machine translation using translation memory

In this paper we present a novel way of integrating Translation Memory into an Example-based Machine translation System (EBMT) to deal with the issue of low resources. We have used a dialogue of 380 sentences as the example-base for our system. The translation units in the Translation Memories are automatically extracted based on the aligned phrases (words) of a statistical machine translation (SMT) system. We attempt to use the approach to improve translation from English to Bangla as many statistical machine translation systems have difficulty with such small amounts of training data. We have found the approach shows improvement over a baseline SMT system

A statistical simulation technique to develop and evaluate conversational agents

In this paper, we present a technique for developing user simulators which are able to interact and evaluate conversational agents. Our technique is based on a statistical model that is automatically learned from a dialog corpus. This model is used by the user simulator to provide the next answer taking into account the complete history of the interaction. The main objective of our proposal is not only to evaluate the conversational agent, but also to improve this agent by employing the simulated dialogs to learn a better dialog model. We have applied this technique to design and evaluate a conversational agent which provides academic information in a multi-agent system. The results of the evaluation show that the proposed user simulation methodology can be used not only to evaluate conversational agents but also to explore new enhanced dialog strategies, thereby allowing the conversational agent to reduce the time needed to complete the dialogs and automatically detect new valid paths to achieve each of the required objectives defined for the task.This work was supported in part by Projects MINECO TEC2012-37832-C02-01, CICYT TEC 2011-28626-C02-02, CAM CONTEXTS (S2009/TIC-1485).Publicad

Short Text Pre-training with Extended Token Classification for E-commerce Query Understanding

E-commerce query understanding is the process of inferring the shopping intent of customers by extracting semantic meaning from their search queries. The recent progress of pre-trained masked language models (MLM) in natural language processing is extremely attractive for developing effective query understanding models. Specifically, MLM learns contextual text embedding via recovering the masked tokens in the sentences. Such a pre-training process relies on the sufficient contextual information. It is, however, less effective for search queries, which are usually short text. When applying masking to short search queries, most contextual information is lost and the intent of the search queries may be changed. To mitigate the above issues for MLM pre-training on search queries, we propose a novel pre-training task specifically designed for short text, called Extended Token Classification (ETC). Instead of masking the input text, our approach extends the input by inserting tokens via a generator network, and trains a discriminator to identify which tokens are inserted in the extended input. We conduct experiments in an E-commerce store to demonstrate the effectiveness of ETC