Papers by Jack Weston
Queens are Powerful too: Mitigating Gender Bias in Dialogue Generation (2020.emnlp-main)
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| Challenge: | Social biases present in data are often directly reflected in the predictions of models trained on that data. |
| Approach: | They analyze gender bias in dialogue data and propose techniques to mitigate it . they use counterfactual data augmentation, targeted data collection, and bias controlled training . |
| Outcome: | The proposed techniques mitigate gender bias by balancing genderedness of generated dialogue utterances. |
Am I Me or You? State-of-the-Art Dialogue Models Cannot Maintain an Identity (2022.findings-naacl)
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| Challenge: | State-of-the-art open-domain dialogue models fail to maintain character identity throughout discourse . despite improvements in accuracy and self-contradiction, agents take on the role of interlocutor . |
| Approach: | They formalize and quantify the deficiency in character identity modeling by using human evaluations. |
| Outcome: | The proposed models reduce mistaken identity issues by nearly 65% according to human annotators while improving engagingness. |
Generative Pretraining for Paraphrase Evaluation (2022.acl-long)
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| Challenge: | ParaBLEU is a paraphrase representation learning model and evaluation metric for text generation. |
| Approach: | They propose a paraphrase representation learning model and evaluation metric for text generation that uses generative conditioning as a pretraining objective. |
| Outcome: | The proposed model outperforms existing models on the 2017 WMT Metrics Shared Task using only 50% of the available training data and surpasses BLEU, ROUGE and METEOR with only 40 examples. |
How to Motivate Your Dragon: Teaching Goal-Driven Agents to Speak and Act in Fantasy Worlds (2021.naacl-main)
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| Challenge: | a recent improvement in the quality of natural language processing and generation (NLG) is needed for goal-oriented ML driven agents. |
| Approach: | They propose a reinforcement learning system that integrates large-scale language modeling and commonsense reasoning-based pre-training to imbue the agent with relevant priors. |
| Outcome: | The proposed system is able to act and talk naturally with respect to their motivations. |
Reason first, then respond: Modular Generation for Knowledge-infused Dialogue (2022.findings-emnlp)
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| Challenge: | Large language models can produce fluent dialogue but often hallucinate factual inaccuracies. |
| Approach: | They propose a modular model for incorporating knowledge into conversational agents that generates a knowledge sequence and then attends to its own generated knowledge sequence. |
| Outcome: | The proposed model hallucinates less in knowledge-grounded dialogue tasks and has advantages in terms of interpretability and modularity. |
Dialogue in the Wild: Learning from a Deployed Role-Playing Game with Humans and Bots (2021.findings-acl)
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| Challenge: | prevailing paradigm in natural language processing research is to build a fixed dataset and freeze it, without any ability for the model to interact with humans using language at training time at all. |
| Approach: | They build and deploy a role-playing game where players converse with learning agents situated in an open-domain fantasy world. |
| Outcome: | The proposed game enables human players to learn from human conversations and improves on their models. |
TOOLVERIFIER: Generalization to New Tools via Self-Verification (2024.findings-emnlp)
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Dheeraj Mekala, Jason Weston, Jack Lanchantin, Roberta Raileanu, Maria Lomeli, Jingbo Shang, Jane Dwivedi-Yu
| Challenge: | Existing tools and APIs present a challenge for generalization, despite frequent parameter updates and the daily introduction of new tools. |
| Approach: | They propose a method which distinguishes between close candidates by self-asking contrastive questions during tool selection and parameter generation. |
| Outcome: | Experiments on 4 tasks from the ToolBench benchmark show an improvement of 22% over few-shot baselines. |
Learning to Speak and Act in a Fantasy Text Adventure Game (D19-1)
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Jack Urbanek, Angela Fan, Siddharth Karamcheti, Saachi Jain, Samuel Humeau, Emily Dinan, Tim Rocktäschel, Douwe Kiela, Arthur Szlam, Jason Weston
| Challenge: | Existing studies on grounded dialogue use only statistical regularities of text data, without explicit understanding of the world that the text describes. |
| Approach: | They propose a large-scale crowdsourced text adventure game as a research platform for studying grounded dialogue. |
| Outcome: | The proposed game allows agents to perceive, emote, and act whilst conducting dialogue with other agents. |
Personalizing Dialogue Agents: I have a dog, do you have pets too? (P18-1)
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| Challenge: | chit-chat models lack specificity, do not display a consistent personality and are often not very captivating. |
| Approach: | They propose to train chit-chat models to condition on profile information and profile information about the interlocutors. |
| Outcome: | The proposed model can predict profile information about the interlocutors based on the data . the proposed model is able to generate meaningful responses in a chit-chat setting . |