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- Explainable AI: A Brief Survey on History, Research Areas, Approaches and Challenges693
The history of Explainable AI is introduced, starting from expert systems and traditional machine learning approaches to the latest progress in the context of modern deep learning, and the major research areas and the state-of-art approaches in recent years are described.
- Style Transfer in Text: Exploration and Evaluation550
This work proposes two novel evaluation metrics that measure two aspects of style transfer: transfer strength and content preservation, and shows that the proposed content preservation metric is highly correlate to human judgments.
- Multi-grained Attention Network for Aspect-Level Sentiment Classification520
Experimental results show that the multi-grained attention network consistently outperforms the state-of-the-art methods on all three datasets, and the effectiveness of aspect alignment loss indicates the aspect-level interactions can bring extra useful information and further improve the performance.
- 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.
- Semantic Relation Classification via Convolutional Neural Networks with Simple Negative Sampling304
This paper proposes to learn more robust relation representations from shortest dependency paths through a convolution neural network, takes the relation directionality into account and proposes a straightforward negative sampling strategy to improve the assignment of subjects and objects.
- Question Answering on Freebase via Relation Extraction and Textual Evidence303
This work first presents a neural network based relation extractor to retrieve the candidate answers from Freebase, and then infer over Wikipedia to validate these answers, a substantial improvement over the state-of-the-art.
- gStore302
This paper proposes a graph-based approach to store and query RDF data, which can answer exact SPARQL queries and queries with wildcards in a uniform manner, and proposes an effective maintenance algorithm to handle online updates over RDF repositories.
- Natural language question answering over RDF274
A semantic query graph is proposed to model the query intention in the natural language question in a structural way, based on which, RDF Q/A is reduced to subgraph matching problem and resolves the ambiguity of natural language questions at the time when matches of query are found.
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- 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).
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- 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.
- Jointly Learning Entity and Relation Representations for Entity Alignment190
This paper presents a novel joint learning framework for entity alignment that is a Graph Convolutional Network (GCN) based framework for learning both entity and relation representations and incorporates the relation approximation into entities to iteratively learn better representations for both.
- 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.
- Neighborhood Matching Network for Entity Alignment151
Neighborhood Matching Network (NMN), a novel entity alignment framework for tackling the structural heterogeneity challenge, can well estimate the neighborhood similarity in more tough cases and significantly outperforms 12 previous state-of-the-art methods.
- 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.
- Processing SPARQL queries over distributed RDF graphs124
This work proposes techniques for processing SPARQL queries over a large RDF graph in a distributed environment using a “partial evaluation and assembly” framework that is superior to the state-of-the-art methods in both the system’s performance and scalability.
- A Convolution BiLSTM Neural Network Model for Chinese Event Extraction122
This paper first comes up with the language specific issue in Chinese event extraction, and then proposes a convolution bidirectional L STM neural network that combines LSTM and CNN to capture both sentence-level and lexical information without any hand-craft features.
- 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.
- Semantic SPARQL similarity search over RDF knowledge graphs108
This paper proposes an effective framework to access the RDF repository even if users have no full knowledge of the underlying schema, and is the first to propose a novel similarity measure, semantic graph edit distance, to improve the efficiency performance.
- VideoHallucer: Evaluating Intrinsic and Extrinsic Hallucinations in Large Video-Language Models99
This work introduces VideoHallucer, the first comprehensive benchmark for hallucination detection in large video-language models (LVLMs), which categorizes hallucinations into two main types: intrinsic and extrinsic, offering further subcategories for detailed analysis.
- Answering Natural Language Questions via Phrasal Semantic Parsing99
An efficient pipeline framework to model a user’s query intention as a phrase level dependency DAG which is then instantiated regarding a specific KB to construct the final structured query is proposed.
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 2026-10-16. 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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