Context aware guidance for multimedia authoring: harmonizing domain and discourse knowledge

This paper presents an approach to assist authors during the authoring of multimedia presentations. We extend existing authoring support by integrating processes of topic identification, content collection and discourse structure building in a single environment. This integration allows identification of the context of the authoring process. Our approach combines this process context awareness with explicit domain and discourse knowledge to steer system suggestions. We evaluate our approach with an experimental system prototyp

Demonstratives, referent identification and topicality in Wambon and some other Papuan languages

Abstract In Papuan languages like Wambon and Urim demonstrative forms are used both in contexts of referent identification, e.g. as demonstrative operators in noun phrases, and in topicality contexts, e.g. as topic markers with adverbial clauses and phrases, recapitulative clauses, new topic NPs and given topic NPs. Using notions from the Functional Grammar framework (Dik, 1989), I present a non-unified account of the demonstrative forms: helping the addressee to identify referents by giving deictic hints like ‘close to speaker’ and orienting the addressee about the topical cohesion of the discourse are two separate functional domains in language. This ‘two-domain’ hypothesis, which views the demonstrative forms as having two synchronically unrelated functions, explains the fact that in Wambon and Urim the demonstratives show important differences in form and behaviour depending on whether they are used for referent identification or for expressing topicality distinctions. The ‘two-domain’ hypothesis explains such formal differences but cannot explain the formal similarities between topic markers and demonstrative operators in Papuan languages like Wambon and Urim. To explain these formal similarities I suggest a diachronic development: in several Papuan languages topic markers developed from demonstrative operators. In the relatively well-documented Awyu-family of Papuan languages this process can be traced: in Wambon, the resumptive demonstrative pronoun- eve integrated in the preceding NP as a topic marker in stative clauses with a very transparant dichotomous topic-comment structure. In Korowai, also of the Awyu-family, the clitic -efè, function as a demonstrative operator and functions solely as a topic marker

Tentative Reference Acts? ‘Recognitional Demonstratives’ as Means of Suggesting Mutual Knowledge – or Overriding a Lack of It

In an explorative study on German oral corpus data we investigate recognitional use of proximal demonstratives as a means of explicit speaker-hearer interaction shaping the discourse structure. We show that recognitionals mark tentative reference acts in that speakers suggest - or pretend - mutual knowledge of the referent, at the same time appealing to the hearers to accept the reference. Hearers may tacitly or explicitly accept the referential act or deny it asking for clarification, in the latter case making speakers change the intended local discourse topic. On these grounds we argue against a differentiation between recognitional and indefinite demonstratives, subsuming both as kinds of recognitional use under ‘pretended’ cognitive proximity

Topic Independent Identification of Agreement and Disagreement in Social Media Dialogue

Research on the structure of dialogue has been hampered for years because large dialogue corpora have not been available. This has impacted the dialogue research community's ability to develop better theories, as well as good off the shelf tools for dialogue processing. Happily, an increasing amount of information and opinion exchange occur in natural dialogue in online forums, where people share their opinions about a vast range of topics. In particular we are interested in rejection in dialogue, also called disagreement and denial, where the size of available dialogue corpora, for the first time, offers an opportunity to empirically test theoretical accounts of the expression and inference of rejection in dialogue. In this paper, we test whether topic-independent features motivated by theoretical predictions can be used to recognize rejection in online forums in a topic independent way. Our results show that our theoretically motivated features achieve 66% accuracy, an improvement over a unigram baseline of an absolute 6%.Comment: @inproceedings{Misra2013TopicII, title={Topic Independent Identification of Agreement and Disagreement in Social Media Dialogue}, author={Amita Misra and Marilyn A. Walker}, booktitle={SIGDIAL Conference}, year={2013}

How did the discussion go: Discourse act classification in social media conversations

We propose a novel attention based hierarchical LSTM model to classify discourse act sequences in social media conversations, aimed at mining data from online discussion using textual meanings beyond sentence level. The very uniqueness of the task is the complete categorization of possible pragmatic roles in informal textual discussions, contrary to extraction of question-answers, stance detection or sarcasm identification which are very much role specific tasks. Early attempt was made on a Reddit discussion dataset. We train our model on the same data, and present test results on two different datasets, one from Reddit and one from Facebook. Our proposed model outperformed the previous one in terms of domain independence; without using platform-dependent structural features, our hierarchical LSTM with word relevance attention mechanism achieved F1-scores of 71\% and 66\% respectively to predict discourse roles of comments in Reddit and Facebook discussions. Efficiency of recurrent and convolutional architectures in order to learn discursive representation on the same task has been presented and analyzed, with different word and comment embedding schemes. Our attention mechanism enables us to inquire into relevance ordering of text segments according to their roles in discourse. We present a human annotator experiment to unveil important observations about modeling and data annotation. Equipped with our text-based discourse identification model, we inquire into how heterogeneous non-textual features like location, time, leaning of information etc. play their roles in charaterizing online discussions on Facebook

Parsing Argumentation Structures in Persuasive Essays

In this article, we present a novel approach for parsing argumentation structures. We identify argument components using sequence labeling at the token level and apply a new joint model for detecting argumentation structures. The proposed model globally optimizes argument component types and argumentative relations using integer linear programming. We show that our model considerably improves the performance of base classifiers and significantly outperforms challenging heuristic baselines. Moreover, we introduce a novel corpus of persuasive essays annotated with argumentation structures. We show that our annotation scheme and annotation guidelines successfully guide human annotators to substantial agreement. This corpus and the annotation guidelines are freely available for ensuring reproducibility and to encourage future research in computational argumentation.Comment: Under review in Computational Linguistics. First submission: 26 October 2015. Revised submission: 15 July 201

Follow-up question handling in the IMIX and Ritel systems: A comparative study

One of the basic topics of question answering (QA) dialogue systems is how follow-up questions should be interpreted by a QA system. In this paper, we shall discuss our experience with the IMIX and Ritel systems, for both of which a follow-up question handling scheme has been developed, and corpora have been collected. These two systems are each other's opposites in many respects: IMIX is multimodal, non-factoid, black-box QA, while Ritel is speech, factoid, keyword-based QA. Nevertheless, we will show that they are quite comparable, and that it is fruitful to examine the similarities and differences. We shall look at how the systems are composed, and how real, non-expert, users interact with the systems. We shall also provide comparisons with systems from the literature where possible, and indicate where open issues lie and in what areas existing systems may be improved. We conclude that most systems have a common architecture with a set of common subtasks, in particular detecting follow-up questions and finding referents for them. We characterise these tasks using the typical techniques used for performing them, and data from our corpora. We also identify a special type of follow-up question, the discourse question, which is asked when the user is trying to understand an answer, and propose some basic methods for handling it

A Topic-Agnostic Approach for Identifying Fake News Pages

Fake news and misinformation have been increasingly used to manipulate popular opinion and influence political processes. To better understand fake news, how they are propagated, and how to counter their effect, it is necessary to first identify them. Recently, approaches have been proposed to automatically classify articles as fake based on their content. An important challenge for these approaches comes from the dynamic nature of news: as new political events are covered, topics and discourse constantly change and thus, a classifier trained using content from articles published at a given time is likely to become ineffective in the future. To address this challenge, we propose a topic-agnostic (TAG) classification strategy that uses linguistic and web-markup features to identify fake news pages. We report experimental results using multiple data sets which show that our approach attains high accuracy in the identification of fake news, even as topics evolve over time.Comment: Accepted for publication in the Companion Proceedings of the 2019 World Wide Web Conference (WWW'19 Companion). Presented in the 2019 International Workshop on Misinformation, Computational Fact-Checking and Credible Web (MisinfoWorkshop2019). 6 page

Communication difficulties following right hemisphere stroke : applying evidence to clinical management

Following reports in the 1960s that language may be affected by right hemisphere (RH) lesions, many limitations to effective communication in the right hemisphere damaged (RHD) population have been described and evidenced. However, stereotypical portrayals and descriptions of carefully selected cases may be misleading as to the extent of communication deficits. In many of the parameters in which RHD patients are presented as typically impaired, e.g. discourse skills, a less severe picture may emerge where data from the non-brain damaged (NBD) population are considered, with age and education variables controlled. Subsequent to RHD, some people show deficit on some communication measures, but many of these communication behaviours are also present in some NBD adults. Thus diagnosis of deficit must be made with reference both to the healthy peer population and the individual's pre-lesion behaviour. The authors' right RH stroke research programme includes studies of incidence of communication deficit, comparisons of RHD and NBD groups in various spoken discourse and comprehension tasks, comparison of RHD groups of different ages, detailed analysis of topic within discourse in RHD and NBD groups, family members' views of communication behaviour following RHD, and the natural course of communication change during the first year after RH stroke. The findings from several studies are summarised and used as the basis for management recommendations, which may guide future outcome research. There is an urgent need for the evaluation of communication management programmes, to determine whether therapists may with confidence offer an effective intervention service to those people whose communication skills are affected by RHD