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Works236 from public data
- Joint Event Extraction via Structured Prediction with Global Features590
This work proposes a joint framework based on structured prediction which extracts triggers and arguments together so that the local predictions can be mutually improved, and proposes to incorporate global features which explicitly capture the dependencies of multiple triggers and argued.
- Incremental Joint Extraction of Entity Mentions and Relations535
An incremental joint framework to simultaneously extract entity mentions and relations using structured perceptron with efficient beam-search is presented, which significantly outperforms a strong pipelined baseline, which attains better performance than the best-reported end-to-end system.
- Refining Event Extraction through Cross-Document Inference515
This work designs a simple scheme to conduct cross-document inference for improving the ACE event extraction task, and applies the hypothesis of “One Sense Per Discourse” to information extraction.
- A Joint Neural Model for Information Extraction with Global Features481
A joint neural framework that aims to extract the globally optimal IE result as a graph from an input sentence and can be easily applied to new languages or trained in a multilingual manner, as OneIE does not use any language-specific feature.
- Knowledge Base Population: Successful Approaches and Challenges402
The techniques which can serve as a basis for a good KBP system are provided, the remaining challenges by comparison with traditional Information Extraction (IE) and Question Answering (QA) tasks are laid out, and some suggestions to address these challenges are provided.
- Towards a Unified Multi-Dimensional Evaluator for Text Generation397
This paper re-frame NLG evaluation as a Boolean Question Answering (QA) task, and by guiding the model with different questions, they can use one evaluator to evaluate from multiple dimensions, and introduces an intermediate learning phase that enables UniEval to incorporate external knowledge from multiple related tasks and gain further improvement.
- Document-Level Event Argument Extraction by Conditional Generation391
A document-level neural event argument extraction model is proposed by formulating the task as conditional generation following event templates by creating the first end-to-end zero-shot event extraction framework.
- MINT: Evaluating LLMs in Multi-turn Interaction with Tools and Language Feedback386
MINT is introduced, a benchmark that evaluates LLMs' ability to solve tasks with multi-turn interactions by (1) using tools and (2) leveraging natural language feedback, and repurpose a diverse set of established evaluation datasets focusing on reasoning, coding, and decision-making into a compact subset for efficient evaluation.
- Unleashing the Emergent Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration330
This work proposes Solo Performance Prompting (SPP), which transforms a single LLM into a cognitive synergist by engaging in multi-turn self-collaboration with multiple personas and shows that assigning multiple fine-grained personas in LLMs improves problem-solving abilities compared to using a single or fixed number of personas.
- CoType320
A novel domain-independent framework that jointly embeds entity mentions, relation mentions, text features and type labels into two low-dimensional spaces, and adopts a novel partial-label loss function for noisy labeled data and introduces an object "translation" function to capture the cross-constraints of entities and relations on each other.
- Visual Attention Model for Name Tagging in Multimodal Social Media314
This paper creates two new multimodal datasets and proposes a novel model architecture based on Visual Attention that not only provides deeper visual understanding on the decisions of the model, but also significantly outperforms other state-of-the-art baseline methods for this task.
- Moralization in social networks and the emergence of violence during protests228
By analysing the language of tweets around protests in Baltimore in 2015 and through behavioural laboratory experiments, Dehghani and colleagues find that moralization of protest issues leads to greater support for violence and increased incidence of violent protest.
- Zero-Shot Transfer Learning for Event Extraction220
A transferable architecture of structural and compositional neural networks is designed to jointly represent and map event mentions and types into a shared semantic space and can select, for each event mention, the event type which is semantically closest in this space as its type.
- FaitCrowd199
Experimental results on two real-world datasets show that FaitCrowd can significantly reduce the error rate of aggregation compared with the state-of-the-art multi-source aggregation approaches due to its ability of learning topical expertise from question content and collected answers.
- A Novel Neural Topic Model and Its Supervised Extension173
A novel neural topic model (NTM) is proposed where the representation of words and documents are efficiently and naturally combined into a uniform framework and is competitive in both topic discovery and classification/regression tasks.
- Revisiting Out-of-distribution Robustness in NLP: Benchmark, Analysis, and LLMs Evaluations163
This paper reexamines the research on out-of-distribution (OOD) robustness in the field of NLP and proposes a benchmark construction protocol that ensures clear differentiation and challenging distribution shifts.
- CRAFT: Customizing LLMs by Creating and Retrieving from Specialized Toolsets154
CRAFT is designed to be flexible and offers a plug-and-play approach to adapt off-the-shelf LLMs to unseen domains and modalities, without any finetuning, and achieves substantial improvements compared to strong baselines.
- LM-Infinite: Zero-Shot Extreme Length Generalization for Large Language Models151
LM-Infinite is a simple and effective method for enhancing LLMs’ capabilities of handling long contexts, which allows LLMs pre-trained with 2K or 4K-long segments to generalize to up to 200M length inputs while retaining perplexity.
- Keep Meeting Summaries on Topic: Abstractive Multi-Modal Meeting Summarization149
An abstractive meeting summarizer from both videos and audios of meeting recordings is developed, which significantly outperforms the state-of-the-art with both BLEU and ROUGE measures.
- Label Noise Reduction in Entity Typing by Heterogeneous Partial-Label Embedding149
A global objective is formulated for learning the embeddings from text corpora and knowledge bases, which adopts a novel margin-based loss that is robust to noisy labels and faithfully models type correlation derived from knowledge bases.
- 147
- AFET: Automatic Fine-Grained Entity Typing by Hierarchical Partial-Label Embedding146
This paper proposes a novel embedding method to separately model “clean” and “noisy” mentions, and incorporates the given type hierarchy to induce loss functions.
- 141
- R-Tuning: Instructing Large Language Models to Say ‘I Don’t Know’137
Refusal-Aware Instruction Tuning is presented, formalized by first identifying the disparity in knowledge encompassed by pre-trained parameters compared to that of instruction tuning data, and constructing the refusal-aware data based on the knowledge intersection to tune LLMs to refrain from responding to questions beyond its parametric knowledge.
- Cross-media Structured Common Space for Multimedia Event Extraction133
A novel method is proposed, Weakly Aligned Structured Embedding (WASE), that encodes structured representations of semantic information from textual and visual data into a common embedding space that enables exploiting available resources without explicit cross-media annotation.
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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