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In this presentation I show first results from an ongoing study about syntactic complexity of sanctioning turns in spoken language. This study is part of a larger project on sanctioning of misconduct in social interaction in different European languages (English, German, Italian and Polish). For the study I use video recordings of different everyday settings (family breakfasts, board game interactions and car rides) with three or four participants. These data come from the Parallel European Corpus of Informal Interaction (Kornfeld/Küttner/Zinken 2023; Küttner et al. submitted). I focus on sanctioning turns with more than one turn-constructional unit (see among others for TCUs: Sacks/Schegloff/Jefferson 1974; Clayman 2013). The study asks how often TCUs are linked to each other in the different languages, for what function, and how language diversity enters into this. Note that complex sanctioning turns do not always come as complex sentences.
The QUEST (QUality ESTablished) project aims at ensuring the reusability of audio-visual datasets (Wamprechtshammer et al., 2022) by devising quality criteria and curating processes. RefCo (Reference Corpora) is an initiative within QUEST in collaboration with DoReCo (Documentation Reference Corpus, Paschen et al. (2020)) focusing on language documentation projects. Previously, Aznar and Seifart (2020) introduced a set of quality criteria dedicated to documenting fieldwork corpora. Based on these criteria, we establish a semi-automatic review process for existing and work-in-progress corpora, in particular for language documentation. The goal is to improve the quality of a corpus by increasing its reusability. A central part of this process is a template for machine-readable corpus documentation and automatic data verification based on this documentation. In addition to the documentation and automatic verification, the process involves a human review and potentially results in a RefCo certification of the corpus. For each of these steps, we provide guidelines and manuals. We describe the evaluation process in detail, highlight the current limits for automatic evaluation and how the manual review is organized accordingly.
This contribution investigates the use of the Czech particle jako (“like”/“as”) in naturally occurring conversations. Inspired by interactional research on unfinished or suspended utterances and on turn-final conjunctions and particles, the analysis aims to trace the possible development of jako from conjunction to a tag-like particle that can be exploited for mobilizing affiliative responses. Traditionally, jako has been described as conjunction used for comparing two elements or for providing a specification of a first element [“X (is) like Y”]. In spoken Czech, however, jako can be flexibly positioned within a speaking turn and does not seem to operate as a coordinating or hypotactic conjunction. As a result, prior studies have described jako as a polyfunctional particle. This article will try to shed light on the meaning of jako in spoken discourse by focusing on its apparent fuzzy or “filler” uses, i.e., when it is found in a mid-turn position in multi-unit turns and in the immediate vicinity of hesitations, pauses, and turn suspensions. Based on examples from mundane, video-recorded conversations and on a sequential and multimodal approach to social interaction, the analyses will first show that jako frequently frames discursive objects that co-participants should respond to. By using jako before a pause and concurrently adopting specific embodied displays, participants can more explicitly seek to mobilize responsive action. Moreover, as jako tends to cluster in multi-unit turns involving the formulation of subjective experience or stance, it can be shown to be specifically designed for mobilizing affiliative responses. Finally, it will be argued that the potential of jako to open up interactive turn spaces can be linked to the fundamental comparative semantics of the original conjunction.
We apply a decision tree based approach to pronoun resolution in spoken dialogue. Our system deals with pronouns with NP- and non-NP-antecedents. We present a set of features designed for pronoun resolution in spoken dialogue and determine the most promising features. We evaluate the system on twenty Switchboard dialogues and show that it compares well to Byron’s (2002) manually tuned system.
This paper describes the TEI-based ISO standard 24624:2016 ‘Transcription of spoken language’ and other formats used within CLARIN for spoken language resources. It assesses the current state of support for the standard and the interoperability between these formats and with rele- vant tools and services. The main idea behind the paper is that a digital infrastructure providing language resources and services to researchers should also allow the combined use of resources and/or services from different contexts. This requires syntactic and semantic interoperability. We propose a solution based on the ISO/TEI format and describe the necessary steps for this format to work as an exchange format with basic semantic interoperability for spoken language resources across the CLARIN infrastructure and beyond.
We present an implemented machine learning system for the automatic detection of nonreferential it in spoken dialog. The system builds on shallow features extracted from dialog transcripts. Our experiments indicate a level of performance that makes the system usable as a preprocessing filter for a coreference resolution system. We also report results of an annotation study dealing with the classification of it by naive subjects.
In this paper, we address two problems in indexing and querying spoken language corpora with overlapping speaker contributions. First, we look into how token distance and token precedence can be measured when multiple primary data streams are available and when transcriptions happen to be tokenized, but are not synchronized with the sound at the level of individual tokens. We propose and experiment with a speaker based search mode that enables any speaker’s transcription tier to be the basic tokenization layer whereby the contributions of other speakers are mapped to this given tier. Secondly, we address two distinct methods of how speaker overlaps can be captured in the TEI based ISO Standard for Spoken Language Transcriptions (ISO 24624:2016) and how they can be queried by MTAS – an open source Lucene-based search engine for querying text with multilevel annotations. We illustrate the problems, introduce possible solutions and discuss their benefits and drawbacks.
In this paper we investigate the coverage of the two knowledge sources WordNet and Wikipedia for the task of bridging resolution. We report on an annotation experiment which yielded pairs of bridging anaphors and their antecedents in spoken multi-party dialog. Manual inspection of the two knowledge sources showed that, with some interesting exceptions, Wikipedia is superior to WordNet when it comes to the coverage of information necessary to resolve the bridging anaphors in our data set. We further describe a simple procedure for the automatic extraction of the required knowledge from Wikipedia by means of an API, and discuss some of the implications of the procedure’s performance.
In German oral discourse, previous research has shown that okay can be used both as a response token (e.g., for agreeing with the previous turn or for claiming a certain degree of understanding) and as a discourse marker (e.g., for closing conversational topics or sequences and/or indicating transitions). This contribution focuses on the use of okay as a response token and how it is connected with the speakers’ interactional state of knowledge (their understanding, their assumptions etc.). The analysis is based on video recorded everyday conversations in German and a sequential, micro-analytic approach (multimodal conversation analysis). The main function of conversational okay in the selected data set is related to indicating the acceptance of prior information. By okay, speakers however claim acceptance of a piece of information that they can’t verify or check. The analysis contrasts different sequences containing okay only with sequences in which change-of-state tokens such as ah and achso co-occur with okay. This illustrates that okay itself does not index prior information as new, and that it is not used for agreeing with or for confirming prior information. Instead it enables the speaker to adopt a kind of neutral, “non-agreeing” position towards a given piece of information.