DecOp: A Multilingual and Multi-domain Corpus For Detecting Deception In Typed Text
15484Authors
nan
Abstract
AbstractIn recent years, the increasing interest in the development of automatic approaches for unmasking deception in online sources led to promising results. Nonetheless, among the others, two major issues remain still unsolved: the stability of classifiers performances across different domains and languages. Tackling these issues is challenging since labelled corpora involving multiple domains and compiled in more than one language are few in the scientific literature. For filling this gap, in this paper we introduce DecOp (Deceptive Opinions), a new language resource developed for automatic deception detection in cross-domain and cross-language scenarios. DecOp is composed of 5000 examples of both truthful and deceitful first-person opinions balanced both across five different domains and two languages and, to the best of our knowledge, is the largest corpus allowing cross-domain and cross-language comparisons in deceit detection tasks. In this paper, we describe the collection procedure of the DecOp corpus and his main characteristics. Moreover, the human performance on the DecOp test-set and preliminary experiments by means of machine learning models based on Transformer architecture are shown.
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Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020), pages 133–137 Marseille, 11–16 May 2020 c⃝ European Language Resources Association (ELRA), licensed under CC-BY-NC 133 Cortical Speech Databases for Deciphering the Articulatory Code Harald Höge Universität der Bundeswehr München harald.hoege@t-online.de Abstract The paper relates to following ‘AC-hypotheses’: The articulatory code (AC) is a neural code exchanging multi-item messages between the short-term memory and cortical areas as the vSMC and STG. In these areas already neurons active in the presence of articulatory features have been measured. The AC codes the content of speech segmented in chunks and is the same for both modalities - speech perception and speech production. Each AC-message is related to a syllable. The items of each message relate to coordinated articulatory gestures composing the syllable. The mechanism to transport the AC and to segment the auditory signal is based on Ɵ/γ- oscillations, where a Ɵ-cycle has the duration of a Ɵ-syllable. The paper describes the findings from neuroscience, phonetics and the science of evolution leading to the AC-hypotheses. The paper proposes to verify the AC-hypotheses by measuring the activity of all ensembles of neurons coding and decoding the AC. Due to state of the art, the cortical measurements to be prepared, done and further processed need a high effort from scientists active in different areas. We propose to launch a project to produce cortical speech databases with cortical recordings synchronized with the speech signal allowing to decipher the articulatory code. Keywords: articulatory code, speech perception, speech production, auditory cortex, ventral sensory cortex, short-term memory 1. Introduction Deciphering the neural code of sensors and actors and deciphering the mechanism of transporting the neural code between the short-term memory1 and sensor/actor processing neural areas are challenging fields in neuroscience. We regard a special neural code which transports multi-item messages, where each message corresponds to an object and each item corresponds to the parts composing the object. Large progress has been done in understanding the mechanism to transport multi-item messages between distant neural areas (Lisman and Jensen, 2013). The transport is performed by ϴ/ɤ- oscillations. Each ϴ-cycle ports the message of an object. Embedded in each ϴ-cycle are ɤ-cycles porting the parts of the object. The porting mechanism is performed by ϴ/ɤ-oscillations modulated with the neural spike-patterns. Thus, to decipher a neural code porting multi-item messages, following knowledge must be gained: the complete set of the parts (items) of all objects for each message the ϴ/ɤ-structure (position of the items within a ϴ-cycle) the spike-patterns of the items In rare cases a multi-item neural code has been deciphered. An example is the code generated by ‘cell- place’ neurons (O’Keefe and Dostrovsky, 1971). This neural code is used to determine the spatial position of a rat. The paper regards a special multi-item neural code - the articulatory code (AC). It is hypothesized that the AC is the same for both modalities: speech perception and speech production. The objects of its messages are syllables, the parts …
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Labeled Text
AbstractIn recent years, the increasing interest in the development of automatic approaches for unmasking deception in online sources led to promising results. Nonetheless, among the others, two major issues remain still unsolved: the stability of classifiers performances across different domains and languages. Tackling these issues is challenging since labelled corpora involving multiple domains and compiled in more than one language are few in the scientific literature. For filling this gap, in this paper we introduce DecOp (Deceptive Opinions), a new language resource developed for automatic deception detection in cross-domain and cross-language scenarios. DecOp is composed of 5000 examples of both truthful and deceitful first-person opinions balanced both across five different domains and two languages and, to the best of our knowledge, is the largest corpus allowing cross-domain and cross-language comparisons in deceit detection tasks. In this paper, we describe the collection procedure of the DecOp corpus and his main characteristics. Moreover, the human performance on the DecOp test-set and preliminary experiments by means of machine learning models based on Transformer architecture are shown.
Publication Details
- Paper ID:
- 15484
- Year:
- 2020
- Publication:
- TIPSTER
- Publication Type:
- conf
- DOI:
- nan