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Found 68 papers, 26 papers with code
Building a Fine-Grained Entity Typing System Overnight for a New X (X = Language, Domain, Genre)
Recent research has shown great progress on fine-grained entity typing.
Bridge Text and Knowledge by Learning Multi-Prototype Entity Mention Embedding
Integrating text and knowledge into a unified semantic space has attracted significant research interests recently.
Zero-Shot Transfer Learning for Event Extraction
Most previous event extraction studies have relied heavily on features derived from annotated event mentions, thus cannot be applied to new event types without annotation effort.
Improving Slot Filling Performance with Attentive Neural Networks on Dependency Structures
Slot Filling (SF) aims to extract the values of certain types of attributes (or slots, such as person:cities\_of\_residence) for a given entity from a large collection of source documents.
Learning Phrase Embeddings from Paraphrases with GRUs
Learning phrase representations has been widely explored in many Natural Language Processing (NLP) tasks (e. g., Sentiment Analysis, Machine Translation) and has shown promising improvements.
Open Relation Extraction and Grounding
Previous open Relation Extraction (open RE) approaches mainly rely on linguistic patterns and constraints to extract important relational triples from large-scale corpora.
Entity-aware Image Caption Generation
Current image captioning approaches generate descriptions which lack specific information, such as named entities that are involved in the images.
Multi-lingual Common Semantic Space Construction via Cluster-consistent Word Embedding
We construct a multilingual common semantic space based on distributional semantics, where words from multiple languages are projected into a shared space to enable knowledge and resource transfer across languages.
Paper Abstract Writing through Editing Mechanism
We present a paper abstract writing system based on an attentive neural sequence-to-sequence model that can take a title as input and automatically generate an abstract.
Ranked #1 on
Paper generation
on ACL Title and Abstract Dataset
Tracking State Changes in Procedural Text: A Challenge Dataset and Models for Process Paragraph Comprehension
The new dataset, ProPara, is the first to contain natural (rather than machine-generated) text about a changing world along with a full annotation of entity states (location and existence) during those changes (81k datapoints).
Ranked #4 on
Procedural Text Understanding
on ProPara
Chengyu Cloze Test
We present a neural recommendation model for Chengyu, which is a special type of Chinese idiom.
Describing a Knowledge Base
We aim to automatically generate natural language descriptions about an input structured knowledge base (KB).
Genre Separation Network with Adversarial Training for Cross-genre Relation Extraction
Relation Extraction suffers from dramatical performance decrease when training a model on one genre and directly applying it to a new genre, due to the distinct feature distributions.
PaperRobot: Incremental Draft Generation of Scientific Ideas
We present a PaperRobot who performs as an automatic research assistant by (1) conducting deep understanding of a large collection of human-written papers in a target domain and constructing comprehensive background knowledge graphs (KGs); (2) creating new ideas by predicting links from the background KGs, by combining graph attention and contextual text attention; (3) incrementally writing some key elements of a new paper based on memory-attention networks: from the input title along with predicted related entities to generate a paper abstract, from the abstract to generate conclusion and future work, and finally from future work to generate a title for a follow-on paper.
Biomedical Event Extraction based on Knowledge-driven Tree-LSTM
Event extraction for the biomedical domain is more challenging than that in the general news domain since it requires broader acquisition of domain-specific knowledge and deeper understanding of complex contexts.
Cross-lingual Multi-Level Adversarial Transfer to Enhance Low-Resource Name Tagging
We focus on improving name tagging for low-resource languages using annotations from related languages.
Cosmos QA: Machine Reading Comprehension with Contextual Commonsense Reasoning
In this paper, we introduce Cosmos QA, a large-scale dataset of 35, 600 problems that require commonsense-based reading comprehension, formulated as multiple-choice questions.
ReviewRobot: Explainable Paper Review Generation based on Knowledge Synthesis
To assist human review process, we build a novel ReviewRobot to automatically assign a review score and write comments for multiple categories such as novelty and meaningful comparison.
Global Attention for Name Tagging
Many name tagging approaches use local contextual information with much success, but fail when the local context is ambiguous or limited.
The Future is not One-dimensional: Complex Event Schema Induction by Graph Modeling for Event Prediction
We introduce a new concept of Temporal Complex Event Schema: a graph-based schema representation that encompasses events, arguments, temporal connections and argument relations.
Membership Inference Attacks on Knowledge Graphs
Membership inference attacks (MIAs) infer whether a specific data record is used for target model training.
Extracting Temporal Event Relation with Syntax-guided Graph Transformer
Extracting temporal relations (e. g., before, after, and simultaneous) among events is crucial to natural language understanding.
How Knowledge Graph and Attention Help? A Quantitative Analysis into Bag-level Relation Extraction
In this paper, we contribute a dataset and propose a paradigm to quantitatively evaluate the effect of attention and KG on bag-level relation extraction (RE).
How Knowledge Graph and Attention Help? A Qualitative Analysis into Bag-level Relation Extraction
In this paper, we contribute a dataset and propose a paradigm to quantitatively evaluate the effect of attention and KG on bag-level relation extraction (RE).
Query and Extract: Refining Event Extraction as Type-oriented Binary Decoding
Event extraction is typically modeled as a multi-class classification problem where event types and argument roles are treated as atomic symbols.
MuMuQA: Multimedia Multi-Hop News Question Answering via Cross-Media Knowledge Extraction and Grounding
Specifically, the task involves multi-hop questions that require reasoning over image-caption pairs to identify the grounded visual object being referred to and then predicting a span from the news body text to answer the question.
What Makes the Story Forward? Inferring Commonsense Explanations as Prompts for Future Event Generation
To alleviate the knowledge forgetting issue, we design two modules, Im and Gm, for each type of knowledge, which are combined via prompt tuning.
Efficient Long Sequence Encoding via Synchronization
Pre-trained Transformer models have achieved successes in a wide range of NLP tasks, but are inefficient when dealing with long input sequences.
PLANET: Dynamic Content Planning in Autoregressive Transformers for Long-form Text Generation
Despite recent progress of pre-trained language models on generating fluent text, existing methods still suffer from incoherence problems in long-form text generation tasks that require proper content control and planning to form a coherent high-level logical flow.
Efficient Federated Learning on Knowledge Graphs via Privacy-preserving Relation Embedding Aggregation
Furthermore, we propose a Federated learning paradigm with privacy-preserving Relation embedding aggregation (FedR) to tackle the privacy issue in FedE.
The Art of Prompting: Event Detection based on Type Specific Prompts
We compare various forms of prompts to represent event types and develop a unified framework to incorporate the event type specific prompts for supervised, few-shot, and zero-shot event detection.
Identifying and Measuring Token-Level Sentiment Bias in Pre-trained Language Models with Prompts
Due to the superior performance, large-scale pre-trained language models (PLMs) have been widely adopted in many aspects of human society.
Incremental Prompting: Episodic Memory Prompt for Lifelong Event Detection
Lifelong event detection aims to incrementally update a model with new event types and data while retaining the capability on previously learned old types.
Probing Script Knowledge from Pre-Trained Models
Script knowledge is critical for humans to understand the broad daily tasks and routine activities in the world.
Improve Event Extraction via Self-Training with Gradient Guidance
Data scarcity has been the main factor that hinders the progress of event extraction.
End-to-End Multimodal Fact-Checking and Explanation Generation: A Challenging Dataset and Models
We propose end-to-end multimodal fact-checking and explanation generation, where the input is a claim and a large collection of web sources, including articles, images, videos, and tweets, and the goal is to assess the truthfulness of the claim by retrieving relevant evidence and predicting a truthfulness label (e. g., support, refute or not enough information), and to generate a statement to summarize and explain the reasoning and ruling process.
Multimedia Generative Script Learning for Task Planning
Goal-oriented generative script learning aims to generate subsequent steps to reach a particular goal, which is an essential task to assist robots or humans in performing stereotypical activities.
MOCHA: A Multi-Task Training Approach for Coherent Text Generation from Cognitive Perspective
Teaching neural models to generate narrative coherent texts is a critical problem.
MultiInstruct: Improving Multi-Modal Zero-Shot Learning via Instruction Tuning
Our results indicate that fine-tuning the model on a diverse set of tasks and instructions leads to a reduced sensitivity to variations in instructions for each task.
Rationalization for Explainable NLP: A Survey
Recent improvements in natural language generation have made rationalization an attractive technique because it is intuitive, human-comprehensible, and accessible to non-technical users.
Explainable Artificial Intelligence (XAI)
Question Answering
+3
ExClaim: Explainable Neural Claim Verification Using Rationalization
We introduce a novel claim verification approach, namely: ExClaim, that attempts to provide an explainable claim verification system with foundational evidence.
Understand the Dynamic World: An End-to-End Knowledge Informed Framework for Open Domain Entity State Tracking
Open domain entity state tracking aims to predict reasonable state changes of entities (i. e., [attribute] of [entity] was [before_state] and [after_state] afterwards) given the action descriptions.
The Art of SOCRATIC QUESTIONING: Recursive Thinking with Large Language Models
Chain-of-Thought (CoT) prompting enables large language models to solve complex reasoning problems by generating intermediate steps.
RE$^2$: Region-Aware Relation Extraction from Visually Rich Documents
Current research in form understanding predominantly relies on large pre-trained language models, necessitating extensive data for pre-training.
AMELI: Enhancing Multimodal Entity Linking with Fine-Grained Attributes
We propose attribute-aware multimodal entity linking, where the input is a mention described with a text and image, and the goal is to predict the corresponding target entity from a multimodal knowledge base (KB) where each entity is also described with a text description, a visual image and a set of attributes and values.
Iteratively Improving Biomedical Entity Linking and Event Extraction via Hard Expectation-Maximization
To solve these challenges, we propose joint biomedical entity linking and event extraction by regarding the event structures and entity references in knowledge bases as latent variables and updating the two task-specific models in a hard Expectation-Maximization (EM) fashion: (1) predicting the missing variables for each partially annotated dataset based on the current two task-specific models, and (2) updating the parameters of each model on the corresponding pseudo completed dataset.
Teamwork Is Not Always Good: An Empirical Study of Classifier Drift in Class-incremental Information Extraction
Class-incremental learning (CIL) aims to develop a learning system that can continually learn new classes from a data stream without forgetting previously learned classes.
A Survey of Document-Level Information Extraction
Document-level information extraction (IE) is a crucial task in natural language processing (NLP).
Efficient Federated Prompt Tuning for Black-box Large Pre-trained Models
With the blowout development of pre-trained models (PTMs), the efficient tuning of these models for diverse downstream applications has emerged as a pivotal research concern.
MULTISCRIPT: Multimodal Script Learning for Supporting Open Domain Everyday Tasks
Automatically generating scripts (i. e. sequences of key steps described in text) from video demonstrations and reasoning about the subsequent steps are crucial to the modern AI virtual assistants to guide humans to complete everyday tasks, especially unfamiliar ones.
X-Eval: Generalizable Multi-aspect Text Evaluation via Augmented Instruction Tuning with Auxiliary Evaluation Aspects
Natural Language Generation (NLG) typically involves evaluating the generated text in various aspects (e. g., consistency and naturalness) to obtain a comprehensive assessment.
RoAST: Robustifying Language Models via Adversarial Perturbation with Selective Training
Under a unified evaluation of fine-tuned LMs by incorporating four representative perspectives of model robustness, we demonstrate the effectiveness of RoAST compared to state-of-the-art fine-tuning methods on six different types of LMs, which indicates its usefulness in practice.
TrustLLM: Trustworthiness in Large Language Models
This paper introduces TrustLLM, a comprehensive study of trustworthiness in LLMs, including principles for different dimensions of trustworthiness, established benchmark, evaluation, and analysis of trustworthiness for mainstream LLMs, and discussion of open challenges and future directions.
Facing the Elephant in the Room: Visual Prompt Tuning or Full Finetuning?
As the scale of vision models continues to grow, the emergence of Visual Prompt Tuning (VPT) as a parameter-efficient transfer learning technique has gained attention due to its superior performance compared to traditional full-finetuning.
Navigating the Dual Facets: A Comprehensive Evaluation of Sequential Memory Editing in Large Language Models
Our in-depth study advocates more careful use of ME in real-world scenarios.
Vision-Flan: Scaling Human-Labeled Tasks in Visual Instruction Tuning
Despite vision-language models' (VLMs) remarkable capabilities as versatile visual assistants, two substantial challenges persist within the existing VLM frameworks: (1) lacking task diversity in pretraining and visual instruction tuning, and (2) annotation error and bias in GPT-4 synthesized instruction tuning data.
Multimodal Instruction Tuning with Conditional Mixture of LoRA
Multimodal Large Language Models (MLLMs) have demonstrated remarkable proficiency in diverse tasks across different domains, with an increasing focus on improving their zero-shot generalization capabilities for unseen multimodal tasks.
X-Shot: A Unified System to Handle Frequent, Few-shot and Zero-shot Learning Simultaneously in Classification
In recent years, few-shot and zero-shot learning, which learn to predict labels with limited annotated instances, have garnered significant attention.
Many-to-many Image Generation with Auto-regressive Diffusion Models
This paper introduces a domain-general framework for many-to-many image generation, capable of producing interrelated image series from a given set of images, offering a scalable solution that obviates the need for task-specific solutions across different multi-image scenarios.
New Frontiers of Information Extraction
This tutorial targets researchers and practitioners who are interested in AI and ML technologies for structural information extraction (IE) from unstructured textual sources.
Semi-supervised New Event Type Induction and Event Detection
We design a Semi-Supervised Vector Quantized Variational Autoencoder framework to automatically learn a discrete latent type representation for each seen and unseen type and optimize them using seen type event annotations.
Towards Automatic Curation of Antibiotic Resistance Genes via Statement Extraction from Scientific Papers: A Benchmark Dataset and Models
Antibiotic resistance has become a growing worldwide concern as new resistance mechanisms are emerging and spreading globally, and thus detecting and collecting the cause – Antibiotic Resistance Genes (ARGs), have been more critical than ever.
