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Found 10 papers, 3 papers with code
Mixed Preference Optimization: Reinforcement Learning with Data Selection and Better Reference Model
By analyzing the stability and robustness of RLHF and DPO, we propose MPO (Mixed Preference Optimization), a novel method that mitigates the weaknesses of both approaches.
Mitigating the Impact of False Negatives in Dense Retrieval with Contrastive Confidence Regularization
Hard negative sampling, which is commonly used to improve contrastive learning, can introduce more noise in training.
Long Short-Term Planning for Conversational Recommendation Systems
Specifically, the recommendation predicts the long-term recommendation target based on the conversational context and the user history.
Improving Question Generation with Multi-level Content Planning
Previous studies have suggested that key phrase selection is essential for question generation (QG), yet it is still challenging to connect such disjointed phrases into meaningful questions, particularly for long context.
Revisiting the Role of Similarity and Dissimilarity in Best Counter Argument Retrieval
Following the definition that the best counter-argument addresses the same aspects as the input argument while having the opposite stance, we aim to develop an efficient and effective model for scoring counter-arguments based on similarity and dissimilarity metrics.
Coarse-to-Fine Knowledge Selection for Document Grounded Dialogs
Specifically, the former efficiently finds relevant passages in a retrieval-and-reranking process, whereas the latter effectively extracts finer-grain spans within those passages to incorporate into a parametric answer generation model (BART, T5).
Doc2Bot: Accessing Heterogeneous Documents via Conversational Bots
This paper introduces Doc2Bot, a novel dataset for building machines that help users seek information via conversations.
Dave the debater: a retrieval-based and generative argumentative dialogue agent
In this paper, we explore the problem of developing an argumentative dialogue agent that can be able to discuss with human users on controversial topics.
Attentive Neural Network for Named Entity Recognition in Vietnamese
We propose an attentive neural network for the task of named entity recognition in Vietnamese.
Joint learning of frequency and word embeddings for multilingual readability assessment
This paper describes two models that employ word frequency embeddings to deal with the problem of readability assessment in multiple languages.
