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Found 17 papers, 8 papers with code
Towards Safety and Helpfulness Balanced Responses via Controllable Large Language Models
As large language models (LLMs) become easily accessible nowadays, the trade-off between safety and helpfulness can significantly impact user experience.
CausalDialogue: Modeling Utterance-level Causality in Conversations
Despite their widespread adoption, neural conversation models have yet to exhibit natural chat capabilities with humans.
Flexible Attention-Based Multi-Policy Fusion for Efficient Deep Reinforcement Learning
In this work, we present Knowledge-Grounded RL (KGRL), an RL paradigm fusing multiple knowledge policies and aiming for human-like efficiency and flexibility.
Dynamic Latent Separation for Deep Learning
A core problem in machine learning is to learn expressive latent variables for model prediction on complex data that involves multiple sub-components in a flexible and interpretable fashion.
FETA: A Benchmark for Few-Sample Task Transfer in Open-Domain Dialogue
Task transfer, transferring knowledge contained in related tasks, holds the promise of reducing the quantity of labeled data required to fine-tune language models.
HybriDialogue: An Information-Seeking Dialogue Dataset Grounded on Tabular and Textual Data
A pressing challenge in current dialogue systems is to successfully converse with users on topics with information distributed across different modalities.
Towards Large-Scale Interpretable Knowledge Graph Reasoning for Dialogue Systems
Our proposed method allows a single transformer model to directly walk on a large-scale knowledge graph to generate responses.
Deep Learning-Based Perceptual Stimulus Encoder for Bionic Vision
Retinal implants have the potential to treat incurable blindness, yet the quality of the artificial vision they produce is still rudimentary.
D-REX: Dialogue Relation Extraction with Explanations
Existing research studies on cross-sentence relation extraction in long-form multi-party conversations aim to improve relation extraction without considering the explainability of such methods.
Ranked #7 on
Dialog Relation Extraction
on DialogRE
Parallelized Reverse Curriculum Generation
For reinforcement learning (RL), it is challenging for an agent to master a task that requires a specific series of actions due to sparse rewards.
Local Explanation of Dialogue Response Generation
To gain insights into the reasoning process of a generation model, we propose a new method, local explanation of response generation (LERG) that regards the explanations as the mutual interaction of segments in input and output sentences.
Quality Estimation without Human-labeled Data
Quality estimation aims to measure the quality of translated content without access to a reference translation.
Knowledge Injection into Dialogue Generation via Language Models
First, we train a large-scale language model and query it as textual knowledge.
DyKgChat: Benchmarking Dialogue Generation Grounding on Dynamic Knowledge Graphs
This paper proposes a new task about how to apply dynamic knowledge graphs in neural conversation model and presents a novel TV series conversation corpus (DyKgChat) for the task.
Proximal Policy Optimization and its Dynamic Version for Sequence Generation
In sequence generation task, many works use policy gradient for model optimization to tackle the intractable backpropagation issue when maximizing the non-differentiable evaluation metrics or fooling the discriminator in adversarial learning.
Improving Conditional Sequence Generative Adversarial Networks by Stepwise Evaluation
To stabilize the training of SeqGAN, Monte Carlo tree search (MCTS) or reward at every generation step (REGS) is used to evaluate the goodness of a generated subsequence.
Transcribing Lyrics From Commercial Song Audio: The First Step Towards Singing Content Processing
Spoken content processing (such as retrieval and browsing) is maturing, but the singing content is still almost completely left out.
