Who’s in, who’s out? Predicting the Inclusiveness or Exclusiveness of Personal Pronouns in Parliamentary Debates (2022.lrec-1)
Copied to clipboard
| Challenge: | clusivity properties of personal pronouns are captured in context, including/excluding audience and/or non-speech act participants. |
| Approach: | They propose a compositional annotation scheme to capture the clusivity properties of personal pronouns in context, which is their ability to construct and manage in-groups and out-group. |
| Outcome: | The proposed schema achieves high inter-annotator agreement with a Cohen’s in the range of 89.7-93.2 and a percentage agreement of > 96%. |
Similar Papers
Out of the Mouths of MPs: Speaker Attribution in Parliamentary Debates (2024.lrec-main)
Copied to clipboard
| Challenge: | Identifying who says what to whom is an essential prerequisite for analysing human communication. |
| Approach: | They propose a new corpus for speaker attribution in german parliamentary debates . the data includes more than 7,700 manually annotated events of speech, thought and writing . they then apply their model to predict speech events in 20 years of debates and investigate the use of factives in the rhetoric of MPs. |
| Outcome: | The proposed model predicts speech events in 20 years of debates and investigates the use of factives in the rhetoric of MPs. |
How to Do Politics with Words: Investigating Speech Acts in Parliamentary Debates (2024.lrec-main)
Copied to clipboard
| Challenge: | a new perspective on framing through the lens of speech acts investigates how politicians make use of different pragmatic speech act functions in political debates. |
| Approach: | They propose a new framework for framing through the lens of speech acts and an annotation scheme for political debates. |
| Outcome: | The proposed framework can predict speech acts with an avg. F1 of around 82.0% . the proposed framework is based on a dataset of German parliamentary debates . |
Overlaps and Gender Analysis in the Context of Broadcast Media (2022.lrec-1)
Copied to clipboard
| Challenge: | Using gender and overlap annotations, we characterise interactions between speakers according to their gender and role in broadcast media. |
| Approach: | They propose to characterise interactions between speakers according to their gender and role in broadcast media by using a small dataset of 93 recordings from LCP French channel. |
| Outcome: | The proposed method could improve the efficiency of qualitative studies conducted in human sciences. |
Our kind of people? Detecting populist references in political debates (2023.findings-eacl)
Copied to clipboard
| Challenge: | Existing literature on populism has only limited agreement on its exact properties . |
| Approach: | They propose a cross-lingual dataset to identify populist rhetoric in text . they propose 'hierarchical' annotation procedure to annotate populist references . |
| Outcome: | The proposed dataset can be used to investigate how political actors talk about The Elite and The People and to study how populist rhetoric is used as a strategic device. |
Who is Speaking? Speaker-Aware Multiparty Dialogue Act Classification (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Identifying how speakers interact with each other in a conversation is difficult when more than two interlocutors take part in . To overcome this challenge, we propose to explicitly add speaker awareness to each utterance representation. |
| Approach: | They propose to add speaker awareness to each utterance representation to model how each speaker is behaving within the local context of a conversation. |
| Outcome: | The proposed approach is able to model multiparticipant and dyadic conversations on the MRDA and SwDA datasets and shows that it is more efficient than previous approaches. |
Annotation and Automatic Classification of Aspectual Categories (P19-1)
Copied to clipboard
| Challenge: | Annotated resource for aspectual classification of German verb tokens in context. |
| Approach: | They present a resource for aspectual classification of German verb tokens in their clausal context. |
| Outcome: | The proposed resource is compared with previous work on German verb tokens using aspectual features compatible with the plurality of aspectual classifications. |
Who Is Speaking to Whom? Learning to Identify Utterance Addressee in Multi-Party Conversations (D19-1)
Copied to clipboard
| Challenge: | In multi-party conversations, addressee information is not always explicit . researchers have spent great efforts to understand conversations between two participants, which is known as multi-part conversation. |
| Approach: | They propose a who-to-whom model which models users and utterances in a conversation session jointly in an interactive way. |
| Outcome: | The proposed model outperforms baseline models on the Ubuntu Multi-Party Conversation Corpus and shows consistent improvements. |
Which Side Are You On? A Multi-task Dataset for End-to-End Argument Summarisation and Evaluation (2024.findings-acl)
Copied to clipboard
Hao Li, Yuping Wu, Viktor Schlegel, Riza Batista-Navarro, Tharindu Madusanka, Iqra Zahid, Jiayan Zeng, Xiaochi Wang, Xinran He, Yizhi Li, Goran Nenadic
| Challenge: | Recent advances in large language models (LLMs) have made it difficult to build an automated debate system that helps people to synthesise persuasive arguments. |
| Approach: | They propose to use an argument mining dataset to capture the end-to-end process of preparing an argumentative essay for a debate. |
| Outcome: | The proposed dataset shows that it performs better on individual tasks than on human-centred evaluations. |
The Subject Annotations of the Danish Parliament Corpus (2009-2017) - Evaluated with Automatic Multi-label Classification (2022.lrec-1)
Copied to clipboard
| Challenge: | The interest in analysing and automatically processing large amounts of political data has increased in the past decades. |
| Approach: | They address the semi-automatic annotation of subjects in the Danish Parliament Corpus (2009-2017) v.2 and describe multi-label classification experiments to verify the consistency of the subject annotation. |
| Outcome: | The proposed method improves on the baseline classifier, which is a majority classifier. |
‘Aye’ or ‘No’? Speech-level Sentiment Analysis of Hansard UK Parliamentary Debate Transcripts (L18-1)
Copied to clipboard
| Challenge: | Transcripts of UK parliamentary debates are difficult for human readers to process due to the large quantity of textual data and the specialised language used. |
| Approach: | They propose to use annotated sentiment labels and labels derived from speakers' votes to classify the sentiment polarity of speakers as being either positive or negative towards motions proposed in the debates. |
| Outcome: | The proposed model outperforms existing models on a dataset of parliamentary debate transcripts using textual and contextual features. |