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- Plan-and-Write: Towards Better Automatic Storytelling515
Experiments show that with explicit storyline planning, the generated stories are more diverse, coherent, and on topic than those generated without creating a full plan, according to both automatic and human evaluations.
- Natural Language Inference by Tree-Based Convolution and Heuristic Matching372
This model, a tree-based convolutional neural network (TBCNN) captures sentence-level semantics; then heuristic matching layers like concatenation, element-wise product/difference combine the information in individual sentences.
- Learning to Respond with Deep Neural Networks for Retrieval-Based Human-Computer Conversation System352
This paper proposes a retrieval-based conversation system with the deep learning-to-respond schema through a deep neural network framework driven by web data and demonstrates significant performance improvement against a series of standard and state-of-art baselines for conversational purposes.
- How Transferable are Neural Networks in NLP Applications?305
In this paper, systematic case studies are conducted and an illuminating picture is provided on the transferability of neural networks in NLP.
- Multi-view Response Selection for Human-Computer Conversation233
Experimental results on a public corpus for context-sensitive response selection demonstrate the effectiveness of the proposed multi-view model, which significantly outperforms other single-view baselines.
- RUBER: An Unsupervised Method for Automatic Evaluation of Open-Domain Dialog Systems228
RUBER is proposed, a Referenced metric and Unreferenced metrics Blended Evaluation Routine, which evaluates a reply by taking into consideration both a groundtruth reply and a query (previous user-issued utterance).
- CGMH: Constrained Sentence Generation by Metropolis-Hastings Sampling216
This paper proposes CGMH, a novel approach using Metropolis-Hastings sampling for constrained sentence generation that allows complicated constraints such as the occurrence of multiple keywords in the target sentences, which cannot be handled in traditional RNN-based approaches.
- Citation count prediction211
This paper has implemented a system which takes a series of features of a particular publication as input and produces as output the estimated citation counts of that article after a given time period and considers several regression models to formulate the learning process and evaluate their performance based on the coefficient of determination (R-square).
- Knowledge-Grounded Dialogue Generation with Pre-trained Language Models206
Empirical results indicate that the proposed response generation defined by a pre-trained language model with a knowledge selection module and an unsupervised approach to jointly optimizing knowledge selection and response generation with unlabeled dialogues can significantly outperform state-of-the-art methods in both automatic evaluation and human judgment.
- Evolutionary timeline summarization205
A novel framework for the web mining problem named Evolutionary Timeline Summarization (ETS), which aims to return the evolution trajectory along the timeline, consisting of individual but correlated summaries of each date, emphasizing relevance, coverage, coherence and cross-date diversity.
- Get The Point of My Utterance! Learning Towards Effective Responses with Multi-Head Attention Mechanism165
A novel Multi-Head Attention Mechanism (MHAM) for generative dialog systems, which aims at capturing multiple semantic aspects from the user utterance is proposed, and a regularizer is formulated to force different attention heads to concentrate on certain aspects.
- OVERCOMING CATASTROPHIC FORGETTING FOR CONTINUAL LEARNING VIA MODEL ADAPTATION155
This paper proposes a very different approach, called Parameter Generation and Model Adaptation (PGMA), to dealing with the problem of catastrophic forgetting in standard neural network architectures.
- Multi-Representation Fusion Network for Multi-Turn Response Selection in Retrieval-Based Chatbots142
This work proposes a multi-representation fusion network where the representations can be fused into matching at an early stage, at an intermediate stage, or at the last stage, and demonstrates the effect of each representation to matching, which sheds light on how to select them in practical systems.
- How to Make Context More Useful? An Empirical Study on Context-Aware Neural Conversational Models133
This paper conducts an empirical study to compare various models and investigate the effect of context information in dialog systems, and proposes a variant that explicitly weights context vectors by context-query relevance, outperforming the other baselines.
- One Time of Interaction May Not Be Enough: Go Deep with an Interaction-over-Interaction Network for Response Selection in Dialogues129
Evaluation results on three benchmark data sets indicate that IoI can significantly outperform state-of-the-art methods in terms of various matching metrics and unveil how the depth of interaction affects the performance of IoI.
- To better stand on the shoulder of giants125
This work learns to identify potentially influential literature via Future Influence Prediction (FIP), which aims to estimate the future influence of literature, and applies the learned model to the application of bibliography recommendation and obtains prominent performance improvement in terms of Mean Average Precision (MAP).
- An Ensemble of Retrieval-Based and Generation-Based Human-Computer Conversation Systems112
Experimental results show that a novel ensemble of retrieval-based and generation-based conversation system outperforms each single module by a large margin.
- i, poet: automatic poetry composition through recurrent neural networks with iterative polishing schema110
This paper forms the poetry composition task as a natural language generation problem using recurrent neural networks, and proposes a new generative model with a polishing schema, and outputs a refined poem composition.
- Tweet Recommendation with Graph Co-Ranking108
A graph-theoretic model for tweet recommendation that presents users with items they may have an interest in and can be parametrized to take into account user preferences, the popularity of tweets and their authors, and diversity.
- VMSMO: Learning to Generate Multimodal Summary for Video-based News Articles96
A Dual-Interaction-based Multimodal Summarizer (DIMS), consisting of a dual interaction module and multimodal generator that handles the semantic relationship between news text and video from a high level.
- Timeline Generation through Evolutionary Trans-Temporal Summarization96
Evaluation results in ROUGE metrics indicate the effectiveness of the proposed approach based on trans-temporal information, and a novel combination of inter-date and intra-date sentence dependencies is presented.
- Learning with Noise: Enhance Distantly Supervised Relation Extraction with Dynamic Transition Matrix94
It is shown that the dynamic transition matrix can effectively characterize the noise in the training data built by distant supervision and can be effectively trained using a novel curriculum learning based method without any direct supervision about the noise.
- "Chitty-Chitty-Chat Bot": Deep Learning for Conversational AI93
This paper focuses on the survey of non-task-oriented chit-chat bots, which can converse seamlessly and appropriately with humans, playing the role of a chat companion.
- Towards Implicit Content-Introducing for Generative Short-Text Conversation Systems92
This paper proposes an implicit content-introducing method which incorporates additional information into the Seq2Seq model in a flexible way and fuse the general decoding and the auxiliary cue word information through the proposed hierarchical gated fusion unit.
- Product-Aware Answer Generation in E-Commerce Question-Answering90
The task of product-aware answer generation, which tends to generate an accurate and complete answer from large-scale unlabeled e-commerce reviews and product attributes is proposed, which is composed of three components: a question-aware review representation module, a key-value memory network encoding attributes, and a recurrent neural network as a sequence generator.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-14. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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