Er ... well, it matters, right? On the role of data representations in spoken language dependency parsing

Dobrovoljc, Kaja; Martinc, Matej

doi:10.18653/v1/w18-6005

Cited by 5 publications

(4 citation statements)

References 19 publications

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“…We are not the first to annotate spoken data. Previous work has annotated English for conversation agents (Davidson et al, 2019), Slovenian data (Dobrovoljc and Martinc, 2018), Komi-Zyrian (Partanen et al, 2018) and Turkish-German (C ¸etinoglu and C ¸öltekin, 2019).…”

Section: Related Workmentioning

confidence: 99%

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Creating a Universal Dependencies Treebank of Spoken Frisian-Dutch Code-switched Data

Braggaar¹,

Goot²

2021

Preprint

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This paper explores the difficulties of annotating transcribed spoken Dutch-Frisian codeswitch utterances into Universal Dependencies. We make use of data from the FAME! corpus, which consists of transcriptions and audio data. Besides the usual annotation difficulties, this dataset is extra challenging because of Frisian being low-resource, the informal nature of the data, code-switching and non-standard sentence segmentation. As a starting point, two annotators annotated 150 random utterances in three stages of 50 utterances. After each stage, disagreements where discussed and resolved. An increase of 7.8 UAS and 10.5 LAS points was achieved between the first and third round. This paper will focus on the issues that arise when annotating a transcribed speech corpus. To resolve these issues several solutions are proposed.

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Section: Related Workmentioning

confidence: 99%

“…Commonly mentioned problems are disfluencies and sentence segmentation (Dobrovoljc and Martinc, 2018). Two main types of solutions can be identified; adapting the existing guidelines (C ¸etinoglu and C ¸öltekin, 2019) versus extending them (Davidson et al, 2019).…”

Section: Related Workmentioning

confidence: 99%

Creating a Universal Dependencies Treebank of Spoken Frisian-Dutch Code-switched Data

Braggaar¹,

Goot²

2021

Preprint

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“…Compared to previous studies, our work goes beyond in several respects (see Section 2). First, compared to most of the other spo-ken dependency treebanks that contain telephone conversations (Bechet et al, 2014), interactions between adults (Dobrovoljc and Martinc, 2018;Dobrovoljc and Nivre, 2016), or user-generated content (Davidson et al, 2019), our dataset attends to child and child-directed speech.…”

Section: Introductionmentioning

confidence: 99%

Data-driven Parsing Evaluation for Child-Parent Interactions

Liu¹,

Prud’hommeaux²

2022

Preprint

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We present a syntactic dependency treebank for naturalistic child and child-directed speech in English (MacWhinney, 2000). Our annotations largely followed the guidelines of the Universal Dependencies project (UD (Zeman et al., 2022)), with detailed extensions to lexical/syntactic structures unique to conversational speech (in opposition to written texts). Compared to existing UD-style spoken treebanks as well as other dependency corpora of child-parent interactions specifically, our dataset is of (much) larger size (N of utterances = 44,744; N of words = 233, 907) and contains speech from a total of 10 children covering a wide age range (18-66 months). With this dataset, we ask: (1) How well would state-of-theart dependency parsers, tailored for the written domain, perform for speech of different interlocutors in spontaneous conversations? (2) What is the relationship between parser performance and the developmental stage of the child? To address these questions, in ongoing work, we are conducting thorough dependency parser evaluations using both graph-based and transition-based parsers with different hyperparameterization, trained from three different types of out-of-domain written texts: news, tweets, and learner data. Our dataset and code are available at https://github.com/ zoeyliu18/childes_parsing.

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“…Specifications for tagging relative pronouns and interrogative particles are insufficiently documented in the MULTEXT-East specifications for Serbo-Croatian, which might have resulted in them being erroneously tagged not only in this, but also in other Serbian and Croatian corpora as well (see srWaC and hrWaC).20 SeeDobrovoljc and Martinc (2018) on the impact of discourse markers on spoken language dependency parsing for Slovene.…”

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confidence: 99%

Converting raw transcripts into an annotated and turn-aligned TEI-XML corpus: the example of the Corpus of Serbian Forms of Address

Lemmenmeier-Batinić

2021

SLO2.0

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This paper describes the procedure of building a TEI-XML corpus of spoken Serbian starting from raw transcripts. The corpus consists of semi–structured interviews, which were gathered with the aim of investigating forms of address in Serbian. The interviews were thoroughly transcribed according to GAT transcribing conventions. However, the transcription was carried out without tools that would control the validity of the GAT syntax, or align the transcript with the audio records. In order to offer this resource to a broader audience, we resolved the inconsistencies in the original transcripts, normalised the semi-orthographic transcriptions and converted the corpus into a TEI-format for transcriptions of speech. Further, we enriched the corpus by tagging and lemmatising the data. Lastly, we aligned the corpus turns to the corresponding audio segments by using a force-alignment tool. In addition to presenting the main steps involved in converting the corpus to the XML-format, this paper also discusses current challenges in the processing of spoken data, and the implications of data re-use regarding transcriptions of speech. This corpus can be used for studying Serbian from the perspective of interactional linguistics, for investigating morphosyntax, grammar, lexicon and phonetics of spoken Serbian, for studying disfluencies, as well as for testing models for automatic speech recognition and forced alignment. The corpus is freely available for research purposes.

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Er ... well, it matters, right? On the role of data representations in spoken language dependency parsing

Cited by 5 publications

References 19 publications

Creating a Universal Dependencies Treebank of Spoken Frisian-Dutch Code-switched Data

Creating a Universal Dependencies Treebank of Spoken Frisian-Dutch Code-switched Data

Data-driven Parsing Evaluation for Child-Parent Interactions

Converting raw transcripts into an annotated and turn-aligned TEI-XML corpus: the example of the Corpus of Serbian Forms of Address

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