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Claim this profileWorks142 from public data
- 1,508
- Target-dependent Twitter Sentiment Classification1,117
This paper proposes to improve target-dependent Twitter sentiment classification by incorporating target- dependent features; and taking related tweets into consideration; and according to the experimental results, this approach greatly improves the performance of target- dependence sentiment classification.
- 861
- Improved Neural Relation Detection for Knowledge Base Question Answering322
This paper proposes a hierarchical recurrent neural network enhanced by residual learning which detects KB relations given an input question and proposes a simple KBQA system that integrates entity linking and the proposed relation detector to make the two components enhance each other.
- Improving Lexical Embeddings with Semantic Knowledge316
This work proposes a new learning objective that incorporates both a neural language model objective (Mikolov et al., 2013) and prior knowledge from semantic resources to learn improved lexical semantic embeddings.
- One-Shot Relational Learning for Knowledge Graphs293
This work proposes a one-shot relational learning framework, which utilizes the knowledge distilled by embedding models and learns a matching metric by considering both the learned embeddings and one-hop graph structures.
- R3: Reinforced Ranker-Reader for Open-Domain Question Answering283
This paper proposes a new pipeline for open-domain QA with a Ranker component, which learns to rank retrieved passages in terms of likelihood of extracting the ground-truth answer to a given question, and proposes a novel method that jointly trains the Ranker along with an answer-extraction Reader model, based on reinforcement learning
- Cross-lingual Knowledge Graph Alignment via Graph Matching Neural Network268
The topic entity graph is introduced, a local sub-graph of an entity, to represent entities with their contextual information in KG, and a graph-attention based solution is proposed that outperforms previous state-of-the-art methods by a large margin.
- Differential Treatment for Stuff and Things: A Simple Unsupervised Domain Adaptation Method for Semantic Segmentation256
The reason why the current adversarial loss is often unstable in minimizing the distribution discrepancy is revealed and it is shown that the method can help ease this issue by minimizing the most similar stuff and instance features between the source and the target domains.
- Diverse Few-Shot Text Classification with Multiple Metrics228
This work proposes an adaptive metric learning approach that automatically determines the best weighted combination from a set of metrics obtained from meta-training tasks for a newly seen few-shot task.
- StoryBuddy: A Human-AI Collaborative Chatbot for Parent-Child Interactive Storytelling with Flexible Parental Involvement219
StoryBuddy is an AI-enabled system for parents to create interactive storytelling experiences and highlighted the need for accommodating dynamic user needs between the desire for parent involvement and parent-child bonding and the goal of minimizing parent intervention when busy.
- Improved Relation Extraction with Feature-Rich Compositional Embedding Models191
A Feature-rich Compositional Embedding Model (FCM) for relation extraction that is expressive, generalizes to new domains, and is easy-to-implement that outperforms both previous compositional models and traditional feature rich models.
- Sentence Embedding Alignment for Lifelong Relation Extraction181
This work utilizes an explicit alignment model to mitigate the sentence embedding distortion of learned model when training on new data and new relations and significantly outperforms the state-of-the-art lifelong learning approaches.
- Improving Natural Language Inference Using External Knowledge in the Science Questions Domain178
A combination of techniques that harness knowledge graphs to improve performance on the NLI problem in the science questions domain and achieves the new state-of-the-art performance over the SciTail science questions dataset.
- Rethinking Cooperative Rationalization: Introspective Extraction and Complement Control163
An introspective model is introduced which explicitly predicts and incorporates the outcome into the selection process and is explicitly control the rationale complement via an adversary so as not to leave any useful information out of the selection.
- FaithDial : A Faithful Benchmark for Information-Seeking Dialogue159
It is found that the benefits of FaithDial generalize to zero-shot transfer on other datasets, such as CMU-Dog and TopicalChat, and human evaluation reveals that responses generated by models trained on FaithDial are perceived as more interpretable, cooperative, and engaging.
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- 137
- Leveraging Sentence-level Information with Encoder LSTM for Semantic Slot Filling131
This paper enhances LSTM-based sequence labeling to explicitly model label dependencies and proposes another enhancement to incorporate the global information spanning over the whole input sequence to predict the label sequence.
- Improving Question Answering over Incomplete KBs with Knowledge-Aware Reader129
A new end-to-end question answering model, which learns to aggregate answer evidence from an incomplete knowledge base (KB) and a set of retrieved text snippets, which achieves consistent improvements across settings with different extents of KB incompleteness.
- Leveraging Abstract Meaning Representation for Knowledge Base Question Answering126
Neuro-Symbolic Question Answering (NSQA) is proposed, a modular KBQA system that leverages Abstract Meaning Representation (AMR) parses for task-independent question understanding and achieves state-of-the-art performance on two prominentKBQA datasets based on DBpedia.
- 119
- Deriving Machine Attention from Human Rationales115
It is demonstrated that even in the low-resource scenario, attention can be learned effectively, and this approach delivers significant gains over state-of-the-art baselines, yielding over 15% average error reduction on benchmark datasets.
- A Co-Matching Model for Multi-choice Reading Comprehension106
A new co-matching approach to this problem, which jointly models whether a passage can match both a question and a candidate answer, achieves state-of-the-art performance.
- Extracting Multiple-Relations in One-Pass with Pre-Trained Transformers104
This work focuses on the task of multiple relation extractions by encoding the paragraph only once, and builds the solution upon the pre-trained self-attentive models (Transformer), where it is shown that the approach is not only scalable but can also perform state-of-the-art on the standard benchmark ACE 2005.
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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