Papers by Stephen Roller
Adding Chit-Chat to Enhance Task-Oriented Dialogues (2021.naacl-main)
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Kai Sun, Seungwhan Moon, Paul Crook, Stephen Roller, Becka Silvert, Bing Liu, Zhiguang Wang, Honglei Liu, Eunjoon Cho, Claire Cardie
| Challenge: | Existing dialogue systems focus on functional goals, open-domain chatbots on socially engaging conversations. |
| Approach: | They propose to add chit-chat to ENhance Task-ORiented dialogues by a human-assisted data collection approach to augment task-oriented dialogues with minimal annotation effort. |
| Outcome: | The proposed models can code-switch between task and chit-chat to be more engaging, interesting, knowledgeable, and humanlike while maintaining competitive task performance. |
Recipes for Building an Open-Domain Chatbot (2021.eacl-main)
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Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu, Jing Xu, Myle Ott, Eric Michael Smith, Y-Lan Boureau, Jason Weston
| Challenge: | Existing work shows that scaling models in the number of parameters and the size of the data they are trained on gives improved results, but other factors are important. |
| Approach: | They propose to build open-domain chatbots that can be scaled to improve their performance . they use a blend of cognitive and cognitive skills to build a model that combines these skills . |
| Outcome: | The proposed models outperform existing approaches in multi-turn dialogue on engagingness and humanness measurements. |
Don’t Say That! Making Inconsistent Dialogue Unlikely with Unlikelihood Training (2020.acl-main)
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| Challenge: | Unlikelihood is a technique developed for removal of repetition in language model completions . it allows for a model to be generalized to solve a number of problems . |
| Approach: | They extend the unlikelihood objective to generate generations that contain repetitions . they show that such an objective can be used to improve logical consistency . |
| Outcome: | The proposed approach can be applied to a number of dialogue tasks. |
Inferring Concept Hierarchies from Text Corpora via Hyperbolic Embeddings (P19-1)
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| Challenge: | Using hyperbolic embeddings, we can infer concept hierarchies from distributional contexts while also being able to predict missing “is-a”-relationships and correct wrong extractions. |
| Approach: | They propose a method combining hyperbolic embeddings and Hearst patterns to set appropriate constraints for inferring “is-a” relationships from large text corpora and improve taxonomic consistency. |
| Outcome: | The proposed method achieves state-of-the-art performance on a variety of hypernymy benchmarks. |
What makes a good conversation? How controllable attributes affect human judgments (N19-1)
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| Challenge: | Existing work on dialogue models for conversational quality is incompletely understanding the relationship between quality and individual attributes. |
| Approach: | They propose to use conditional training and weighted decoding to control four attributes for chit-chat dialogue: repetition, specificity, response-relatedness and question-asking. |
| Outcome: | The proposed methods improve human quality judgments by controlling combinations of these variables. |
The Dialogue Dodecathlon: Open-Domain Knowledge and Image Grounded Conversational Agents (2020.acl-main)
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| Challenge: | a set of 12 tasks that measure if a conversational agent can communicate engagingly with personality and empathy, ask questions, answer questions by utilizing knowledge resources, and perceive and converse about images. |
| Approach: | They propose a set of 12 tasks that measure if a conversational agent can communicate engagingly with personality and empathy . they use large dialogue datasets to multi-task and obtain state-of-the-art results . |
| Outcome: | The proposed model improves over a BERT pre-trained model on large dialogue datasets and provides state-of-the-art results on many of the tasks. |
Language Models that Seek for Knowledge: Modular Search & Generation for Dialogue and Prompt Completion (2022.findings-emnlp)
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| Challenge: | Language models (LMs) have been shown to generate more factual responses by employing modularity in combination with retrieval. |
| Approach: | They extend the recent approach of Adolphs et al. (2021) to include internet search as a module. |
| Outcome: | The proposed method outperforms the state-of-the-art model BlenderBot 2 on open-domain knowledge-grounded conversations for the same number of parameters. |
Leveraging Implicit Feedback from Deployment Data in Dialogue (2024.eacl-short)
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| Challenge: | Xu et al., 2023) and Bai ed., 2019) use crowdworkers to collect signals from natural dialogue episodes. |
| Approach: | They use the publicly released BlenderBot deployment data to extract signals from conversations to implicitly measure the quality of a machine-generated utterance. |
| Outcome: | The proposed model improves over baseline models, but some proxy signals can lead to undesirable generations. |
Hearst Patterns Revisited: Automatic Hypernym Detection from Large Text Corpora (P18-2)
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| Challenge: | a well-known problem of Hearst-like patterns is their extreme sparsity. |
| Approach: | They propose to use pattern-based and distributional methods to perform unsupervised hypernym detection. |
| Outcome: | The proposed method outperforms distributional methods on hypernymy tasks. |