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- Cosmos QA: Machine Reading Comprehension with Contextual Commonsense Reasoning529
This paper introduces Cosmos QA, a large-scale dataset of 35,600 problems that require commonsense-based reading comprehension, formulated as multiple-choice questions, and proposes a new architecture that improves over the competitive baselines.
- 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.
- End-to-End Multimodal Fact-Checking and Explanation Generation: A Challenging Dataset and Models172
The first to build the benchmark dataset and solutions for end-to-end multimodal fact-checking and explanation generation and to establish baseline performances on MOCHEG, it is demonstrated that the performance of the state-of-the-art end- to-end multi-million-dollar fact- checking does not provide satisfactory outcomes.
- Tracking State Changes in Procedural Text: a Challenge Dataset and Models for Process Paragraph Comprehension148
A new dataset and models for comprehending paragraphs about processes, an important genre of text describing a dynamic world, are presented and two new neural models that exploit alternative mechanisms for state prediction are introduced, in particular using LSTM input encoding and span prediction.
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- 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.
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- Bridge Text and Knowledge by Learning Multi-Prototype Entity Mention Embedding99
A novel Multi-Prototype Mention Embedding model is proposed, which learns multiple sense embeddings for each mention by jointly modeling words from textual contexts and entities derived from a knowledge base, and an efficient language model based approach to disambiguate each mention to a specific sense.
- ReviewRobot: Explainable Paper Review Generation based on Knowledge Synthesis91
A novel ReviewRobot is built to automatically assign a review score and write comments for multiple categories such as novelty and meaningful comparison, and can serve as an assistant for paper reviewers, program chairs and authors.
- Entity-aware Image Caption Generation86
A convolutional neural networks - long short term memory networks (CNN-LSTM) model is trained to generate a template caption based on the input image and a knowledge graph based collective inference algorithm is used to fill in the template with specific named entities retrieved via the hashtags.
- Biomedical Event Extraction based on Knowledge-driven Tree-LSTM84
A novel knowledge base (KB)-driven tree-structured long short-term memory networks (Tree-LSTM) framework is proposed, incorporating two new types of features: dependency structures to capture wide contexts and entity properties from external ontologies via entity linking.
- PaperRobot: Incremental Draft Generation of Scientific Ideas80
A PaperRobot who performs as an automatic research assistant by conducting deep understanding of a large collection of human-written papers in a target domain and constructing comprehensive background knowledge graphs is presented.
- Rationalization for explainable NLP: a survey74
This survey presents available methods, explainable evaluations, code, and datasets used across various NLP tasks that use rationalization, and a new subfield in Explainable AI (XAI), namely, Rational AI (RAI), is introduced to advance the current state of rationalization.
- Query and Extract: Refining Event Extraction as Type-oriented Binary Decoding74
This work proposes a novel event extraction framework that uses event types and argument roles as natural language queries to extract candidate triggers and arguments from the input text and significantly outperforms existing methods on zero-shot event extraction.
- The Future is not One-dimensional: Complex Event Schema Induction by Graph Modeling for Event Prediction74
A new concept of Temporal Complex Event Schema is introduced: a graph-based schema representation that encompasses events, arguments, temporal connections and argument relations and a Temporal Event Graph Model that predicts event instances following the temporal complex event schema is proposed.
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- Semi-supervised New Event Type Induction and Event Detection65
A Semi-Supervised Vector Quantized Variational Autoencoder framework is designed to automatically learn a discrete latent type representation for each seen and unseen type and optimize them using seen type event annotations.
- Describing a Knowledge Base59
This work builds a generation framework based on a pointer network which can copy facts from the input KB, and adds two attention mechanisms: (i) slot-aware attention to capture the association between a slot type and its corresponding slot value; and (ii) a new table position self-attention to captured the inter-dependencies among related slots.
- Improving Event Extraction via Multimodal Integration55
This paper first discovers visual patterns from large-scale text-image pairs in a weakly-supervised manner and then proposes a multimodal event extraction algorithm where the event extractor is jointly trained with textual features and visual patterns.
- BLIP3o-NEXT: Next Frontier of Native Image Generation52
BLIP3o-NEXT unifies text-to-image generation and image editing within a single architecture, demonstrating strong image generation and image editing capabilities.
- Open-Schema Event Profiling for Massive News Corpora47
This work develops a Bayesian non-parametric model to detect events and event types by exploiting the slot expressions of the entities mentioned in news articles and proposes an unsupervised embedding model for schema induction that encodes the insight.
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- MuMuQA: Multimedia Multi-Hop News Question Answering via Cross-Media Knowledge Extraction and Grounding37
A new QA evaluation benchmark with 1,384 questions over news articles that require cross- media grounding of objects in images onto text, and introduces a novel multimedia data augmentation framework, based on cross-media knowledge extraction and synthetic question-answer generation, to automatically augment data that can provide weak supervision for this task.
- Optimized Event Storyline Generation based on Mixture-Event-Aspect Model35
A mixture-event-aspect model is proposed which models different sub-events into local and global aspects and utilizes an optimization method to generate the component summaries along the timeline.
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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 2026-10-13. 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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