This paper deals with multi-lingual dialogue act (DA) recognition. The proposed approaches are based on deep neural networks and use word2vec embeddings for word representation. Two multi-lingual models are proposed for this task. The first approach uses one general model trained on the embeddings from all available languages. The second method trains the model on a single pivot language and a linear transformation method is used to project other languages onto the pivot language. The popular convolutional neural network and LSTM architectures with different set-ups are used as classifiers. To the best of our knowledge this is the first attempt at multi-lingual DA recognition using neural networks. The multi-lingual models are validated experimentally on two languages from the Verbmobil corpus.
The above mentioned approaches are mainly evaluated on English language using Switchboard (SwDA) @cite_24 or Meeting Recorder Dialog Act (MRDA) @cite_5 corpora. Some methods are tested on Spanish (see DIHANA @cite_20 corpus), Czech @cite_25 , French @cite_16 or on German (see Verbmobil @cite_7 corpus) languages.
@cite_7 @cite_24 @cite_5 @cite_16 @cite_25 @cite_20
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Abstract : This labeling guide is adapted from work on the Switchboard recordings and the accompanying manual ( 1997). The Switchboard-DAMSL (SWBD-DAMSL) manual for labeling one-on-one phone conversations provided a useful starting point for the types of dialog acts (DAs) that arose in the ICSI meeting corpus. However, the tagset for labeling meetings presented here has been modified as necessary to better reflect the types of interaction we observed in multiparty face-to-face meetings. This guide consists of five major sections: Quick Reference Information, Segmentation, How to Label, Adjacency Pairs, and Tag Descriptions. The first section supplies definitions for terms used throughout this guide and contains the correspondence of the Meeting Recorder DA (MRDA) tagset, which is the tagset detailed within this guide, to the SWBD-DAMSL tagset. This section also contains the entire MRDA tagset organized into groups according to syntactic, semantic, pragmatic, and functional similarities of the utterances they mark. The section entitled Segmentation, as its name indicates, details the rules and guidelines governing what constitutes an utterance along with how to determine utterance boundaries. The third section, How to Label, provides instruction regarding label construction, the management of utterances requiring additional DAs or containing quotes, and the use of the annotation software. The section entitled Adjacency Pairs details how adjacency pairs are constructed and the rules governing their usage. The section entitled Tag Descriptions provides explanations of each tag within the MRDA tagset. Two appendices are also found within this guide. The first provides a labeled portion of a meeting and the second contains information regarding tags used for a select number of meetings. This paper explores the use of bilingual parallel corpora as a source of lexical knowledge for cross-lingual textual entailment. We claim that, in spite of the inherent difficulties of the task, phrase tables extracted from parallel data allow to capture both lexical relations between single words, and contextual information useful for inference. We experiment with a phrasal matching method in order to: i) build a system portable across languages, and ii) evaluate the contribution of lexical knowledge in isolation, without interaction with other inference mechanisms. Results achieved on an English-Spanish corpus obtained from the RTE3 dataset support our claim, with an overall accuracy above average scores reported by RTE participants on monolingual data. Finally, we show that using parallel corpora to extract paraphrase tables reveals their potential also in the monolingual setting, improving the results achieved with other sources of lexical knowledge. This paper deals with automatic dialog acts (DAs) recognition in Czech. The dialog acts are sentence-level labels that represent different states of a dialogue, depending on the application. Our work focuses on two applications: a multimodal reservation system and an animated talking head for hearing-impaired people. In that context, we consider the following DAs: statements, orders, yes no questions and other questions. We propose to use both lexical and prosodic information for DAs recognition. The main goal of this paper is to compare different methods to combine the results of both classifiers. On a Czech corpus simulating a reservation of train tickets, the lexical information only gives about 92 of classification accuracy, while prosody gives only about 45 of accuracy. When both classifiers are combined with a multilayer perceptron, the lowest (lexical) word error rate further decreases by 26 . We show that this improvement is close to the optimal one, given the correlation of the lexical and prosodic features. The other combination schemes do not outperform the lexical-only results. Empirical research on simultaneous interpreting is hampered by the problem of collecting sufficient material (recordings of source speeches and interpreted target speeches) for the testing of hypotheses and validation of existing theories. In other words, corpora have long been awaited in the field of Interpreting Studies. In January 2004 a research group on corpus-based interpreting studies was set up in the Department of Interdiscip linary Studies in Translation, Languages and Cultures (SITLeC) of the University of Bologna at F orli, in order to create an electronic parallel corpus with source and target speeches in Italian, English and Spanish. The main research interest of the group is the study of interpreters’ strategi es across different language directions (directionality) and language-pair related difficul ties. In this paper, the authors illustrate the various stages of development of the EPIC corpus, h ighlighting both research and pedagogical applications of this “multidimensional” tool compri sing video, audio and written materials, which can be retrieved, selected and analyzed by using co rpus linguistics techniques. 2