Papers by Serra Sinem Tekiroğlu
A Computational Exploration of Exaggeration (D18-1)
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| Challenge: | a new computational approach to exaggeration detection is needed for non-literal phenomena . a corpus of overstatements (or hyperboles) is used to detect exaggrements . |
| Approach: | They propose a computational approach to detect exaggerated sentences using crowdsourcing data . they build a corpus containing overstatements and then evaluate models trained on HYPO . |
| Outcome: | The proposed approach can detect exaggerated sentences using a crowdsourced dataset. |
Building a Multilingual Taxonomy of Olfactory Terms with Timestamps (2022.lrec-1)
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| Challenge: | olfactory references play a crucial role in our memory and experiences . but only few works in NLP have attempted to capture this sensory dimension from a computational perspective. |
| Approach: | They describe a process that has led to the semi-automatic development of a taxonomy for olfactory information in four languages (English, French, German and Italian) |
| Outcome: | The proposed taxonomy can be extended using existing language models and n-grams to include olfactory terms in four languages. |
Towards Knowledge-Grounded Counter Narrative Generation for Hate Speech (2021.findings-acl)
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| Challenge: | Existing approaches to combat online hatred using informed textual responses - called counter narratives - produce generic/repetitive responses and lack grounded and up-to-date evidence such as facts, statistics, or examples. |
| Approach: | They propose to automatically generate counter narratives using an external knowledge repository to provide more informative content to fight online hatred. |
| Outcome: | The proposed pipeline can generate suitable and informative counter narratives in in-domain and cross-domain settings. |
Human-Machine Collaboration Approaches to Build a Dialogue Dataset for Hate Speech Countering (2022.emnlp-main)
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| Challenge: | a new approach to combat online hate speech is being proposed for NLG . existing methods to train NLG are limited to 2-turn interactions, while in real life, interactions can consist of multiple turns. |
| Approach: | They propose to combine human annotators with machine generated dialogues to create a dataset . DIALOCONAN is the first dataset comprising over 3000 fictitious multi-turn dialogues . |
| Outcome: | The proposed approach combines human experts over machine generated dialogues . it is the first dataset comprising over 3000 fictitious multi-turn dialogues between a hater and an NGO operator . |
Generating Counter Narratives against Online Hate Speech: Data and Strategies (2020.acl-main)
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| Challenge: | Hate Speech (HS) is a pervasive issue that spreads quickly and widely . research has focused on avoiding undesired effects that come with content moderation . |
| Approach: | They propose to use large scale unsupervised language models to generate responses to hate effectively using large scale models. |
| Outcome: | The proposed methods lack quality data and produce generic/repetitive responses. |
Human-in-the-Loop for Data Collection: a Multi-Target Counter Narrative Dataset to Fight Online Hate Speech (2021.acl-long)
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| Challenge: | Existing studies on generating hate speech/counter narratives have failed to reach high-quality datasets. |
| Approach: | They propose a human-in-the-loop data collection methodology that refines a generative language model iteratively by using its own data from previous loops to generate new training samples. |
| Outcome: | The proposed method is the only expert-based multi-target HS/CN dataset available to the community. |
Using Pre-Trained Language Models for Producing Counter Narratives Against Hate Speech: a Comparative Study (2022.findings-acl)
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| Challenge: | Autoregressive models combined with stochastic decodings are the most promising for generating CNs with regard to an unseen target of hate. |
| Approach: | They propose to use pre-trained language models to generate counter-narratives in English by adding an automatic post-editing step to refine generated CNs. |
| Outcome: | The proposed pipeline could be used to generate counter-narratives in English using pre-trained language models and stochastic decoding mechanisms. |
Toward Stance-based Personas for Opinionated Dialogues (2020.findings-emnlp)
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| Challenge: | chit-chat neural models lacking specificity and coherence, argues a new study on stance-based personas . stancebased personal representations lack generalization capability, allowing agents to sustain personal points of view both within the same conversation and across different discussions. |
| Approach: | They propose to investigate stance-based persona representations and their impact on claim generation by using a conversational dataset. |
| Outcome: | The proposed dataset shows that stance-based personas grasp abstract and profound aspects of the author persona. |
PRODIGy: a PROfile-based DIalogue Generation dataset (2024.findings-naacl)
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| Challenge: | Existing profiles-based dialogue datasets lack explicit profile representations or are difficult to collect. |
| Approach: | They propose a dataset that brings together multiple profiles for each speaker, and then integrates them together to provide a more comprehensive profile dimension set for generative language models. |
| Outcome: | The PRODIGy dataset provides a more comprehensive profile dimension set for each speaker. |
Benchmarking the Generation of Fact Checking Explanations (2023.tacl-1)
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| Challenge: | Automating fact-checking is a time-consuming task that cannot keep up with the ever-increasing amount of fake news produced daily. |
| Approach: | They propose to automate the process of fact-checking by generating justifications from textual explanations of why a claim is classified as either true or false. |
| Outcome: | The proposed approach improves summarization performance over unstructured knowledge and with two datasets with different styles and structures. |