Papers with Reddit comments
Large Scale Multi-Actor Generative Dialog Modeling (2020.acl-main)
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| Challenge: | Non-goal oriented dialog agents typically exhibit inconsistent personality across conversations or the average personality of all users. |
| Approach: | They propose a model that conditionally models past conversations to probabilistically model multi-turn conversations in the actor’s persona. |
| Outcome: | The proposed model improves perplexity on 1.7M held out Reddit conversations by 0.47 on scaling from 117M to 8.3B parameters. |
Unsupervised Neologism Normalization Using Embedding Space Mapping (D19-55)
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| Challenge: | Neologisms refer to recent expressions that are specific to certain entities or events, but have not yet been accepted into mainstream language. |
| Approach: | They propose an unsupervised approach for detecting and normalizing neologisms in social media content without relying on parallel training data. |
| Outcome: | The proposed method detects neologisms and normalizes them to canonical words without training data. |
Learning Universal Authorship Representations (2021.emnlp-main)
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Rafael A. Rivera-Soto, Olivia Elizabeth Miano, Juanita Ordonez, Barry Y. Chen, Aleem Khan, Marcus Bishop, Nicholas Andrews
| Challenge: | authorship verification has traditionally relied on modeling stylometric linguistic properties . but neural methods introduce a tradeoff: they obviate the need for manual feature design . |
| Approach: | They propose to use domain-specific features to improve authorship representations . they propose to study Amazon reviews, fanfiction short stories, and Reddit comments . |
| Outcome: | The proposed methods outperform existing methods in large-scale authorship verification scenarios. |
Representing Social Media Users for Sarcasm Detection (D18-1)
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| Challenge: | Existing annotated corpus of Reddit comments is limited by available annotation methods. |
| Approach: | They propose a Bayesian approach that directly represents authors’ propensities to be sarcastic and a dense embedding approach that can learn interactions between the author and the text. |
| Outcome: | The proposed approach performs better in homogeneous contexts, whereas the dense embeddings prove valuable in more diverse contexts. |
You Are What You Talk About: Inducing Evaluative Topics for Personality Analysis (2022.findings-emnlp)
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| Challenge: | Recent studies have focused on the relationship between personality and evaluative language. |
| Approach: | They propose to map evaluative topics to pre-filtered evalative text and link evalueative topics with individual text authors to build their ev emvaluative profiles. |
| Outcome: | The proposed approach is validated by observing correlations consistent with prior research in personality psychology. |
Contextual Fine-to-Coarse Distillation for Coarse-grained Response Selection in Open-Domain Conversations (2022.acl-long)
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Wei Chen, Yeyun Gong, Can Xu, Huang Hu, Bolun Yao, Zhongyu Wei, Zhihao Fan, Xiaowu Hu, Bartuer Zhou, Biao Cheng, Daxin Jiang, Nan Duan
| Challenge: | Existing studies focus on coarse-grained response selection in retrieval-based dialogue systems. |
| Approach: | They propose a Contextual Fine-to-Coarse (CFC) distilled model for coarse-grained response selection in open-domain conversations. |
| Outcome: | The proposed model improves over baseline methods on two datasets based on the Reddit comments dump and Twitter corpus compared with baseline methods. |
Hate Speech and Counter Speech Detection: Conversational Context Does Matter (2022.naacl-main)
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| Challenge: | Existing datasets and models target hate speech but ignore context . Existing models target either hate speech or hate and counter speech but disregard context - a new study shows that context is critical to identify hate and anti-hate speech. |
| Approach: | They propose to use context to identify hate and counter speech in a reddit conversation thread. |
| Outcome: | The proposed model improves when and why context is taken into account. |
BESSTIE: A Benchmark for Sentiment and Sarcasm Classification for Varieties of English (2025.findings-acl)
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| Challenge: | despite large language models showing bias against non-mainstream varieties, there are no labeled datasets for sentiment analysis of English. |
| Approach: | They propose a benchmark for sentiment and sarcasm classification for three varieties of English . they manually annotate the datasets with sentiment and the sarcasmatic labels . |
| Outcome: | The proposed benchmark is based on a web-based content from Google Place reviews and Reddit comments. |
Improving the Detection of Multilingual Online Attacks with Rich Social Media Data from Singapore (2023.acl-long)
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Janosch Haber, Bertie Vidgen, Matthew Chapman, Vibhor Agarwal, Roy Ka-Wei Lee, Yong Keong Yap, Paul Röttger
| Challenge: | Toxic content is a global problem, but most resources for detecting toxic content are in English . new datasets and models for non-English languages focus exclusively on one language or dialect . |
| Approach: | They propose to use a multilingual dataset of online attacks to identify code-mixed toxic content in Singapore . they collect reddit comments in Indonesian, Malay, Singlish, and other languages and provide fine-grained hierarchical labels for attacks . |
| Outcome: | The proposed dataset provides fine-grained hierarchical labels for online attacks in Singapore . it shows that the metadata can be used for granular error analysis . |