Papers by Arthur Szlam
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. |
Reducing Conversational Agents’ Overconfidence Through Linguistic Calibration (2022.tacl-1)
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| Challenge: | Neural generative open-domain english-language dialogue agents are currently unsuitable for applications other than entertainement and research. |
| Approach: | They propose to incorporate metacognitive features into the training of a controllable generation model to improve likelihood of correctness. |
| Outcome: | The proposed model improves likelihood of correctness by incorporating metacognitive features into the training of a controllable generation model. |
Beyond Goldfish Memory: Long-Term Open-Domain Conversation (2022.acl-long)
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| Challenge: | Despite recent improvements in open-domain dialogue models, state of the art models are trained and evaluated on short conversations with little context. |
| Approach: | They propose to use retrieval-augmented methods to summarize and recall past conversations to improve their models. |
| Outcome: | The proposed models outperform the current state-of-the-art models on human-human chat sessions in both automatic and human evaluations. |
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. |
CraftAssist Instruction Parsing: Semantic Parsing for a Voxel-World Assistant (2020.acl-main)
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| Challenge: | Existing datasets for semantic parsing are limited due to the wide variety of robot setups and proprietary nature of personal assistants. |
| Approach: | They propose a semantic parsing dataset for Minecraft's voxel-based crafting game . they propose to use 7K human utterances and their corresponding parses to execute them in game if proper world state is provided . |
| Outcome: | The proposed dataset can be used to analyze human-bot interactions in the game Minecraft. |
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. |
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. |
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. |
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 . |
Dialogue Natural Language Inference (P19-1)
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| Challenge: | Consistency is a long standing issue faced by dialogue models. |
| Approach: | They propose to frame the consistency of dialogue agents as natural language inference and create a new natural language dataset called Dialogue NLI. |
| Outcome: | The proposed model can improve the consistency of a dialogue model with human evaluation and automatic metrics on a suite of evaluation sets designed to measure the model’s consistency. |