Dialogue Games for Crosslingual Communication

We describe a novel approach to crosslingual dialogue that supports highly accurate communication of semantically complex content between people who do not speak the same language. The approach is introduced through an implemented application that covers the same ground as the chapter of a conventional phrase book for food shopping. We position the approach with respect to dialogue systems and Machine Translation-based approaches to crosslingual dialogue. The current work is offered as a first step towards the innovative use of dialogue theories for the enhancement of human–human dialogue

“Appropriateness” in foreign language acquisition and use: some theoretical, methodological and ethical considerations

In this contribution, I focus on the concept of “appropriateness” in the usage, the learning and the teaching of foreign languages. Using a participant-based emic perspective, I investigate multilinguals’ perceptions of appropriateness in their foreign languages. Referring to the existing literature, and using previously unpublished material collected through a web questionnaire (Dewaele and Pavlenko 2001–2003), I will show that multilinguals develop their judgements of appropriateness, a crucial aspect of sociopragmatic and sociocultural competence, as part of their socialisation in a new language/culture. However, their ability to judge appropriateness accurately does not imply that they will always act “appropriately”. Indeed, the presence of conflicting norms in their other languages may contribute to conscious or unconscious divergence from the “appropriate” norm in a particular language. Some implications for foreign language teaching will be considered

Parsing Thai Social Data: A New Challenge for Thai NLP

Dependency parsing (DP) is a task that analyzes text for syntactic structure and relationship between words. DP is widely used to improve natural language processing (NLP) applications in many languages such as English. Previous works on DP are generally applicable to formally written languages. However, they do not apply to informal languages such as the ones used in social networks. Therefore, DP has to be researched and explored with such social network data. In this paper, we explore and identify a DP model that is suitable for Thai social network data. After that, we will identify the appropriate linguistic unit as an input. The result showed that, the transition based model called, improve Elkared dependency parser outperform the others at UAS of 81.42%.Comment: 7 Pages, 8 figures, to be published in The 14th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP 2019

Multimodality and superdiversity: evidence for a research agenda

In recent years, social science research in superdiversity has questioned notions such as multiculturalism and pluralism, which hinge on and de facto reproduce ideological constructs such as separate and clearly identifiable national cultures and ethnic identities; research in language and superdiversity, in translanguaging, polylanguaging and metrolingualism have analogously questioned concepts such as multi- and bi-lingualism, which hinge on ideological constructs such as national languages, mother tongue and native speaker proficiency. Research in multimodality has questioned the centrality of language in everyday communication as well as its paradigmatic role to the understanding of communicative practices. While the multimodality of communication is generally acknowledged in work on language and superdiversity, the potential of a social semiotic multimodal approach for understanding communication in superdiversity has not been adequately explored and developed yet – and neither has the concept of superdiversity been addressed in multimodal research. The present paper wants to start to fill this gap. By discussing sign-making practices in the superdiverse context of Leeds Kirkgate Market (UK), it maps the potentials of an ethnographic social semiotics for the study of communication in superdiversity and sketches an agenda for research on multimodality and superdiversity, identifying a series of working hypotheses, research questions, areas of investigations and domains and fields of enquiry

The emotional weight of "I love you" in multilinguals' languages

The present paper considers the perceived emotional weight of the phrase I love you in multilinguals’ different languages. The sample consists of 1459 adult multilinguals speaking a total of 77 different first languages. They filled out an on-line questionnaire with open and closed questions linked to language behavior and emotions. Feedback on the open question related to perceived emotional weight of the phrase I love you in the multilinguals’ different languages was recoded in three categories: it being strongest in (1) the first language (L1), (2) the first language and a foreign language, and (3) a foreign language (LX). A majority of speakers felt I love you was strongest in their L1. Participants offered various explanations for their perception. Statistical analyses revealed that the perception of weight of the phrase I love you was associated with self-perceived language dominance, context of acquisition of the L2, age of onset of learning the L2, degree of socialization in the L2, nature of the network of interlocutors in the L2, and self-perceived oral proficiency in the L2

Multimodal Classification of Urban Micro-Events

In this paper we seek methods to effectively detect urban micro-events. Urban micro-events are events which occur in cities, have limited geographical coverage and typically affect only a small group of citizens. Because of their scale these are difficult to identify in most data sources. However, by using citizen sensing to gather data, detecting them becomes feasible. The data gathered by citizen sensing is often multimodal and, as a consequence, the information required to detect urban micro-events is distributed over multiple modalities. This makes it essential to have a classifier capable of combining them. In this paper we explore several methods of creating such a classifier, including early, late, hybrid fusion and representation learning using multimodal graphs. We evaluate performance on a real world dataset obtained from a live citizen reporting system. We show that a multimodal approach yields higher performance than unimodal alternatives. Furthermore, we demonstrate that our hybrid combination of early and late fusion with multimodal embeddings performs best in classification of urban micro-events

User requirement elicitation for cross-language information retrieval

Who are the users of a cross-language retrieval system? Under what circumstances do they need to perform such multi-language searches? How will the task and the context of use affect successful interaction with the system? Answers to these questions were explored in a user study performed as part of the design stages of Clarity, a EU founded project on cross-language information retrieval. The findings resulted in a rethink of the planned user interface and a consequent expansion of the set of services offered. This paper reports on the methodology and techniques used for the elicitation of user requirements as well as how these were in turn transformed into new design solutions

Exploring Natural Language Processing and Sentence Embeddings for Sentiment Analysis of Online Restaurant Reviews

This paper explores the application of Natural Language Processing (NLP) methods in sentiment analysis of restaurant reviews available online, for a sample of restaurants in the Algarve region. The primary objective was to develop an automated method that could efficiently extract and categorize relevant sentiments relating to five key attributes of customer satisfaction, namely food quality, service, ambient, price and restaurant’s location. Using the F1 Score the proposed method was compared against human classification benchmarks. The results showed that Universal Sentence Encoding (USE) was a suitable method for implementation due to its acceptable F1 score performance, ease of accessibility and reduced cost. The use of semantic embeddings can provide valuable insights from online reviews that could benefit the restaurant management and in general the data-driven decision-making processes businesses in the gastronomic sector

Uncovering lost potential : the shortcomings of DNBs chatbot

In 2018 DNB Bank ASA (DNB) launched their chatbot, Aino, an advanced virtual banking agent. Aino handles 55% of all the incoming chat traffic for DNBs Customer Center and is continuously being trained by AI trainers to increase the percentage of messages it can respond to. The former CEO of DNB, Rune Bjerke, stated in 2017 that by 2020, 80% of all incoming chat traffic would be handled by chatbots. However, to get closer to this target, DNBs AI trainers will have to make some priorities in the development process. The purpose of this study is to contribute to the decision-making process of which types of problems, and intents the AI trainers should prioritize to reduce DNBs costs. The data basis is conversational logs from conversations between customers of DNB and Aino, in addition to structural interviews with four DNB employees with significant knowledge of Aino. This thesis is a mixed-methods study that consists of both statistical analyses to determine group effect, structured interviews, quantitative content analysis, statistical analyses of chatlogs, as well as analysis of economical impact.I 2018 lanserte DNB Bank ASA (DNB) sin chatbot, Aino, en avansert virtuell bankagent. Aino håndterer 55% av all innkommende chat-trafikk for DNBs kundesenter og blir kontinuerlig opplært av AI-trenere for å øke prosentandelen av meldinger den kan svare på. Den tidligere konsernsjefen i DNB, Rune Bjerke, uttalte i 2017 at innen 2020 ville 80% av all innkommende chat-trafikk bli håndtert av chatbots. For å komme nærmere dette målet, vil DNBs AI-trenere imidlertid måtte gjøre noen prioriteringer i utviklingsprosessen. Hensikten med denne studien er å bidra til beslutningsprosessen for hvilke typer problemer, og intensjoner AI-trenerne bør prioritere for å redusere DNBs kostnader. Datagrunnlaget er samtalelogger fra samtaler mellom kunder av DNB og Aino, i tillegg til strukturerte intervjuer med fire DNB-ansatte med betydelig kunnskap om Aino. Denne oppgaven er et kombinasjonsstudie som består av både statistiske analyser for å bestemme gruppeeffekt, strukturerte intervjuer, kvantitativ innholdsanalyse, statistisk analyse av chatlogger, i tillegg til analyse av finansiell påvirkning.submittedVersionM-Ø