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Found 29 papers, 9 papers with code
Guiding the Release of Safer E2E Conversational AI through Value Sensitive Design
Over the last several years, end-to-end neural conversational agents have vastly improved their ability to carry unrestricted, open-domain conversations with humans.
SafetyKit: First Aid for Measuring Safety in Open-domain Conversational Systems
We then empirically assess the extent to which current tools can measure these effects and current systems display them.
Improving Chess Commentaries by Combining Language Models with Symbolic Reasoning Engines
Despite many recent advancements in language modeling, state-of-the-art language models lack grounding in the real world and struggle with tasks involving complex reasoning.
AutoReply: Detecting Nonsense in Dialogue Introspectively with Discriminative Replies
Existing approaches built separate classifiers to detect nonsense in dialogues.
Human-level play in the game of Diplomacy by combining language models with strategic reasoning
Despite much progress in training AI systems to imitate human language, building agents that use language to communicate intentionally with humans in interactive environments remains a major challenge.
When Life Gives You Lemons, Make Cherryade: Converting Feedback from Bad Responses into Good Labels
Deployed dialogue agents have the potential to integrate human feedback to continuously improve themselves.
Anticipating Safety Issues in E2E Conversational AI: Framework and Tooling
Over the last several years, end-to-end neural conversational agents have vastly improved in their ability to carry a chit-chat conversation with humans.
Bot-Adversarial Dialogue for Safe Conversational Agents
Conversational agents trained on large unlabeled corpora of human interactions will learn patterns and mimic behaviors therein, which include offensive or otherwise toxic behavior.
Reducing conversational agents' overconfidence through linguistic calibration
While improving neural dialogue agents' factual accuracy is the object of much research, another important aspect of communication, less studied in the setting of neural dialogue, is transparency about ignorance.
Recipes for Safety in Open-domain Chatbots
Models trained on large unlabeled corpora of human interactions will learn patterns and mimic behaviors therein, which include offensive or otherwise toxic behavior and unwanted biases.
Controlling Style in Generated Dialogue
Open-domain conversation models have become good at generating natural-sounding dialogue, using very large architectures with billions of trainable parameters.
Deploying Lifelong Open-Domain Dialogue Learning
As argued in de Vries et al. (2020), crowdsourced data has the issues of lack of naturalness and relevance to real-world use cases, while the static dataset paradigm does not allow for a model to learn from its experiences of using language (Silver et al., 2013).
Open-Domain Conversational Agents: Current Progress, Open Problems, and Future Directions
We present our view of what is necessary to build an engaging open-domain conversational agent: covering the qualities of such an agent, the pieces of the puzzle that have been built so far, and the gaping holes we have not filled yet.
Multi-Dimensional Gender Bias Classification
We show our classifiers prove valuable for a variety of important applications, such as controlling for gender bias in generative models, detecting gender bias in arbitrary text, and shed light on offensive language in terms of genderedness.
Recipes for building an open-domain chatbot
Building open-domain chatbots is a challenging area for machine learning research.
I love your chain mail! Making knights smile in a fantasy game world: Open-domain goal-oriented dialogue agents
Dialogue research tends to distinguish between chit-chat and goal-oriented tasks.
Generating Interactive Worlds with Text
We show that the game environments created with our approach are cohesive, diverse, and preferred by human evaluators compared to other machine learning based world construction algorithms.
Zero-Shot Fine-Grained Style Transfer: Leveraging Distributed Continuous Style Representations to Transfer To Unseen Styles
Text style transfer is usually performed using attributes that can take a handful of discrete values (e. g., positive to negative reviews).
Queens are Powerful too: Mitigating Gender Bias in Dialogue Generation
Models often easily learn biases present in the training data, and their predictions directly reflect this bias.
The Dialogue Dodecathlon: Open-Domain Knowledge and Image Grounded Conversational Agents
We introduce dodecaDialogue: a set of 12 tasks that measures if a conversational agent can communicate engagingly with personality and empathy, ask questions, answer questions by utilizing knowledge resources, discuss topics and situations, and perceive and converse about images.
Adversarial NLI: A New Benchmark for Natural Language Understanding
We introduce a new large-scale NLI benchmark dataset, collected via an iterative, adversarial human-and-model-in-the-loop procedure.
Build it Break it Fix it for Dialogue Safety: Robustness from Adversarial Human Attack
The detection of offensive language in the context of a dialogue has become an increasingly important application of natural language processing.
Neural Text Generation with Unlikelihood Training
Neural text generation is a key tool in natural language applications, but it is well known there are major problems at its core.
Learning to Speak and Act in a Fantasy Text Adventure Game
We analyze the ingredients necessary for successful grounding in this setting, and how each of these factors relate to agents that can talk and act successfully.
The Second Conversational Intelligence Challenge (ConvAI2)
We describe the setting and results of the ConvAI2 NeurIPS competition that aims to further the state-of-the-art in open-domain chatbots.
Wizard of Wikipedia: Knowledge-Powered Conversational agents
In open-domain dialogue intelligent agents should exhibit the use of knowledge, however there are few convincing demonstrations of this to date.
Retrieve and Refine: Improved Sequence Generation Models For Dialogue
Sequence generation models for dialogue are known to have several problems: they tend to produce short, generic sentences that are uninformative and unengaging.
Personalizing Dialogue Agents: I have a dog, do you have pets too?
Chit-chat models are known to have several problems: they lack specificity, do not display a consistent personality and are often not very captivating.
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