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Found 27 papers, 8 papers with code
CoLV: A Collaborative Latent Variable Model for Knowledge-Grounded Dialogue Generation
Knowledge-grounded dialogue generation has achieved promising performance with the engagement of external knowledge sources.
Interactive Latent Knowledge Selection for E-Commerce Product Copywriting Generation
As the multi-modal e-commerce is thriving, high-quality advertising product copywriting has gain more attentions, which plays a crucial role in the e-commerce recommender, advertising and even search platforms. The advertising product copywriting is able to enhance the user experience by highlighting the product’s characteristics with textual descriptions and thus to improve the likelihood of user click and purchase.
Adaptive Bridge between Training and Inference for Dialogue Generation
Although exposure bias has been widely studied in some NLP tasks, it faces its unique challenges in dialogue response generation, the representative one-to-various generation scenario. In real human dialogue, there are many appropriate responses for the same context, not only with different expressions, but also with different topics.
From spoken dialogue to formal summary: An utterance rewriting for dialogue summarization
Firstly, an utterance rewriter is conducted to complete the ellipsis content of dialogue content and then obtain the rewriting utterances.
Adaptive Bridge between Training and Inference for Dialogue
Although exposure bias has been widely studied in some NLP tasks, it faces its unique challenges in dialogue response generation, the representative one-to-various generation scenario.
FCM: A Fine-grained Comparison Model for Multi-turn Dialogue Reasoning
Furthermore, the consistency signals between each candidate and the speaker's own history are considered to drive a model to prefer a candidate that is logically consistent with the speaker's history logic.
Identifying Untrustworthy Samples: Data Filtering for Open-domain Dialogues with Bayesian Optimization
The training method updates parameters of a trained NCMs on two small sets with newly maintained and removed samples, respectively.
Topic-Aware Contrastive Learning for Abstractive Dialogue Summarization
To capture the various topic information of a conversation and outline salient facts for the captured topics, this work proposes two topic-aware contrastive learning objectives, namely coherence detection and sub-summary generation objectives, which are expected to implicitly model the topic change and handle information scattering challenges for the dialogue summarization task.
Ranked #3 on
Text Summarization
on SAMSum Corpus
Improving Sequential Recommendation Consistency with Self-Supervised Imitation
In this paper, we propose a model, SSI, to improve sequential recommendation consistency with Self-Supervised Imitation.
Augmenting Knowledge-grounded Conversations with Sequential Knowledge Transition
In particular, a sequential knowledge transition model equipped with a pre-trained knowledge-aware response generator (SKT-KG) formulates the high-level knowledge transition and fully utilizes the limited knowledge data.
Probing Product Description Generation via Posterior Distillation
To tackle this problem, we propose an adaptive posterior network based on Transformer architecture that can utilize user-cared information from customer reviews.
User-Inspired Posterior Network for Recommendation Reason Generation
A simple and effective way is to extract keywords directly from the knowledge-base of products, i. e., attributes or title, as the recommendation reason.
EmpDG: Multi-resolution Interactive Empathetic Dialogue Generation
In response to this problem, we propose a multi-resolution adversarial model {--} EmpDG, to generate more empathetic responses.
Regularizing Dialogue Generation by Imitating Implicit Scenarios
Human dialogues are scenario-based and appropriate responses generally relate to the latent context knowledge entailed by the specific scenario.
Modeling Topical Relevance for Multi-Turn Dialogue Generation
Therefore, an ideal dialogue generation models should be able to capture the topic information of each context, detect the relevant context, and produce appropriate responses accordingly.
Group-wise Contrastive Learning for Neural Dialogue Generation
Neural dialogue response generation has gained much popularity in recent years.
Collaborative Group Learning
Collaborative learning has successfully applied knowledge transfer to guide a pool of small student networks towards robust local minima.
Data Manipulation: Towards Effective Instance Learning for Neural Dialogue Generation via Learning to Augment and Reweight
In this paper, we propose a data manipulation framework to proactively reshape the data distribution towards reliable samples by augmenting and highlighting effective learning samples as well as reducing the effect of inefficient samples simultaneously.
Posterior-GAN: Towards Informative and Coherent Response Generation with Posterior Generative Adversarial Network
Neural conversational models learn to generate responses by taking into account the dialog history.
Learning from Easy to Complex: Adaptive Multi-curricula Learning for Neural Dialogue Generation
Current state-of-the-art neural dialogue systems are mainly data-driven and are trained on human-generated responses.
Adaptive Parameterization for Neural Dialogue Generation
For each conversation, the model generates parameters of the encoder-decoder by referring to the input context.
EmpDG: Multiresolution Interactive Empathetic Dialogue Generation
In response to this problem, we propose a multi-resolution adversarial model -- EmpDG, to generate more empathetic responses.
Explicit State Tracking with Semi-Supervision for Neural Dialogue Generation
However, the \emph{expensive nature of state labeling} and the \emph{weak interpretability} make the dialogue state tracking a challenging problem for both task-oriented and non-task-oriented dialogue generation: For generating responses in task-oriented dialogues, state tracking is usually learned from manually annotated corpora, where the human annotation is expensive for training; for generating responses in non-task-oriented dialogues, most of existing work neglects the explicit state tracking due to the unlimited number of dialogue states.
Knowledge Diffusion for Neural Dialogue Generation
Our empirical study on a real-world dataset prove that our model is capable of generating meaningful, diverse and natural responses for both factoid-questions and knowledge grounded chi-chats.
A Survey on Dialogue Systems: Recent Advances and New Frontiers
Dialogue systems have attracted more and more attention.
