Papers with Reddit data
Pretrain-Finetune Based Training of Task-Oriented Dialogue Systems in a Real-World Setting (2021.naacl-industry)
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| Challenge: | a challenge in building task-oriented dialogue systems is the limited amount of supervised training data available. |
| Approach: | They propose a method for training retrieval-based dialogue systems using annotated data and a larger, unlabeled dataset. |
| Outcome: | The proposed method improves model performance offline and online compared with no pretraining . the model is deployed in an agent-support application and evaluated on live customer service contacts . |
Investigating Wit, Creativity, and Detectability of Large Language Models in Domain-Specific Writing Style Adaptation of Reddit’s Showerthoughts (2024.starsem-1)
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| Challenge: | Recent Large Language Models (LLMs) have shown the ability to generate content that is difficult or impossible to distinguish from human writing. |
| Approach: | They compare GPT-2 and GPT-Neo fine-tuned on Reddit data and GTP-3.5 invoked in a zero-shot manner, against human-authored texts. |
| Outcome: | The proposed model can generate short, creative texts that are difficult to distinguish from human writing, but human evaluators rate them worse than the model. |
Do Word Embeddings Capture Spelling Variation? (2020.coling-main)
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| Challenge: | Using word embeddings, we analyze spelling variation in word embeds trained on Twitter and Reddit data. |
| Approach: | They propose a new perspective on the analysis of word embeddings by focusing on spelling variation. |
| Outcome: | The proposed analysis shows that word embeddings encode spelling variation patterns of various types to some extent, even when trained using the skipgram model. |
CLICK: Contrastive Learning for Injecting Contextual Knowledge to Conversational Recommender System (2023.eacl-main)
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| Challenge: | Existing CRSs lack capturing comprehensive user preferences . existing systems lack contextual knowledge to capture user preferences from a dialogue context . |
| Approach: | They propose a Contrastive Learning approach for Injecting Contextual Knowledge from Reddit data to a CRS task. |
| Outcome: | The proposed approach captures a user preference from a dialogue context without items . it improves on the existing methods, and the results are published in the journal of cognitive science. |
Structuring Latent Spaces for Stylized Response Generation (D19-1)
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| Challenge: | Existing methods for generating responses in a targeted style are limited by the lack of parallel data. |
| Approach: | They propose a method that bridges conversation modeling and non-parallel style transfer by sharing a structured latent space. |
| Outcome: | The proposed system generates responses of the targeted style and outperforms baselines without sacrificing appropriateness. |
Training Data Augmentation for Detecting Adverse Drug Reactions in User-Generated Content (D19-1)
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Sepideh Mesbah, Jie Yang, Robert-Jan Sips, Manuel Valle Torre, Christoph Lofi, Alessandro Bozzon, Geert-Jan Houben
| Challenge: | Existing dictionary-based, semi-supervised learning approaches are limited by the coverage and maintainability of laymen health vocabularies. |
| Approach: | They propose a data augmentation approach that leverages variational autoencoders to learn high-quality data distributions from a large unlabeled dataset and generate a small set of labeled training sets. |
| Outcome: | The proposed approach matches the performance of fully-supervised approaches while requiring only 25% of training data. |
ConVEx: Data-Efficient and Few-Shot Slot Labeling (2021.naacl-main)
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| Challenge: | ConVEx is an efficient pretraining and fine-tuning neural approach for slot-labeling dialog tasks. |
| Approach: | They propose an efficient pretraining and fine-tuning neural approach for slot-labeling dialog tasks that uses a pairwise cloze task and reddit data. |
| Outcome: | The proposed approach is well aligned with its intended use on slot-labeling tasks and can be used across a range of domains and data sets. |
A Semantics-based Approach to Disclosure Classification in User-Generated Online Content (2020.findings-emnlp)
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| Challenge: | Existing algorithms for self-disclosure identification and classification are challenging due to the relative anonymity of social networking sites and lack of non-verbal cues to signal thoughts or feelings. |
| Approach: | They propose an approach to detect emotional and informational self-disclosure in natural language by using frame semantics to identify lexical units and their semantic roles. |
| Outcome: | The proposed method improves on reddit data and provides insights into the drivers of disclosure behaviors. |
Metaphors in Online Religious Communication: A Detailed Dataset and Cross-Genre Metaphor Detection (2024.lrec-main)
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| Challenge: | figurative language plays a particularly important role in religious communication . linguistic metaphors relate entities from different semantic domains by drawing on an implicit similarity between them. |
| Approach: | They present a dataset of fine-grained metaphor annotations for online religious communication . they show that cross-genre transfer metaphor detection leads to a drop in performance . |
| Outcome: | The proposed dataset shows that adding in-genre data improves performance . the authors show that the proposed system can detect metaphors in religious forums . |
RISE: Robust Early-exiting Internal Classifiers for Suicide Risk Evaluation (2024.lrec-main)
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| Challenge: | Existing systems for risk assessment are prone to incorrectly predicting risk severity and have no early detection mechanisms. |
| Approach: | They propose a novel mechanism for accurate early detection of suicide risk by ensembling Hyperbolic Internal Classifiers equipped with an abstention mechanism and early exit inference capabilities. |
| Outcome: | The proposed model abstains from 84% incorrect predictions on Reddit data while out-predicting state of the art models upto 3.5x earlier. |
GerAV: Towards New Heights in German Authorship Verification using Fine-Tuned LLMs on a New Benchmark (2026.findings-acl)
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| Challenge: | Authorship verification (AV) is a task of determining whether two texts were written by the same author. |
| Approach: | They propose a benchmark for German AV comprising over 400k labeled text pairs. |
| Outcome: | The proposed model outperforms baselines and state-of-the-art models by 0.09 and surpasses GPT-5 in a zero-shot setting by 0.08. |