Detecting Attribution Relations in Speech: a Corpus Study
Authors: Alessandra Cervone, Peter Bell, Silvia Pareti, Irina Prodanof, Tommaso Caselli (Università di Pavia, University of Edinburgh, Google Inc., Trento RISE, 2014)
Topic: Multimodal

Abstract
In this work we present a methodology for the annotation of Attri-bution Relations (ARs) in speech which we apply to create a pilot corpus of spo-ken informal dialogues. This represents the first step towards the creation of a re-source for the analysis of ARs in speech and the development of automatic extrac-tion systems. Despite its relevance for speech recognition systems and spoken language understanding, the relation hold-ing between quotations and opinions and their source has been studied and extracted only in written corpora, characterized by a formal register (news, literature, scientific articles). The shift to the informal register and to a spoken corpus widens our view of this relation and poses new challenges. Our hypothesis is that the decreased relia-bility of the linguistic cues found for writ-ten corpora in the fragmented structure of speech could be overcome by including prosodic clues in the system. The analysis of SARC confirms the hypothesis show-ing the crucial role played by the acous-tic level in providing the missing lexical clues.

Introduction
1 Introduction Our everyday conversations are populated by other people’s words, thoughts and opinions. Detect-ing quotations in speech represents the key to “one of the most widespread and fundamental topics of human speech” (Bakhtin, 1981, p. 337). A system able to automatically extract a quo-tation and attribute it to its truthful author from speech would be crucial for many applications. Besides Information Extraction systems aimed at processing spoken documents, it could be useful for Speaker Identification systems, (e.g. the strat-egy of emulating the voice of the reported speaker in quotations could be misunderstood by the sys-tem as a change of speaker). Furthermore, attri-bution extraction could also improve the perfor-mance of Dialogue parsing, Named-Entity Recog-nition and Speech Synthesis tools. On a more ba-sic level, recognizing citations from speech could be useful for sentence boundaries automatic detec-tion systems, where quotations, being sentences embedded in other sentences, could be a source of confusion. So far, however, attribution extraction systems have been developed only for written corpora. Extracting the text span corresponding to quotations and opinions and ascribing it to their proper source within a text means to reconstruct the Attribution Relations (ARs, henceforth) holding between three constitutive elements (following Pareti (2012)): the Source the Cue, i.e. the lexical anchor of the AR (e.g. say, announce, idea) the Content (1) This morning [Source John] [Cue told] me: [Content ”It’s important to support our leader. I trust him.”]. In the past few years ARs extraction has attracted growing attention in NLP for its many potential applications (e.g.Information Extraction, Opinion Mining) while remaining an open challenge. Automatically identifying ARs from a text is a complex task, in particular due to the wide range of syntactic structures that the relation can assume and the lack of a dedicated encoding in the language. While the content boundaries of a direct quotation are explicitly marked by quotation markers, opinions and indirect quotations only partially have syntactically, albeit blurred, boundaries as they can span intersententially. The subtask of identifying the presence of an AR could be tackled with more success by exploiting the presence of the cue as a lexical anchor establishing the links to source and content spans. For this reason, cues are the starting point or a fundamental feature of extraction systems (Pareti et al., 2013; Sarmento and Nunes, 2009; Krestel, 2007). In our previous work (Pareti and Prodanof, 2010; Pareti, 2012), starting from a flexible and comprehensive definition (Pareti and Prodanof, 2010, p. 3566) of AR, we created an annotation scheme which has been used to build the first large annotated resource for attribution, the Penn Attribution Relations Corpus (PARC)1, a corpus of news articles. In order to address the issue of detecting ARs in speech, we started from the theoretical and annotation framework from PARC to create a comparable resource. Section 2 explains the issues connected with extracting ARs from speech. Section 3 describes the Speech Attribution Relations Corpus (SARC, henceforth) and its annotation scheme. The analysis of the corpus is presented in Section 4. Section 5 reports an example of how prosodic cues can be crucial to identify ARs in speech. Finally, Section 6 draws on the conclusions and discusses future work.

Conclusion
6 Conclusions and future work The analysis of SARC, the first resource devel-oped to study ARs in speech, has helped to high-light a major problem of detecting attribution in a spoken corpus: the decreased reliability of the lexical cues crucial in previous approaches (com-pletely useless in at least 10% of the cases) and the consequential need to find reliable prosodic clues to integrate them. The example provided in Section 5 has showed how the integration of the acoustic cues could be useful to improve the accu-racy of attribution detection in speech. As a future project we are going to perform a large acoustic analysis of the ARs found in SARC, in order to see if some reliable prosodic cues can in fact be found and used in order to develop a software able to extract attribution from speech.