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Found 108 papers, 43 papers with code
A Spectral Method for Unsupervised Multi-Document Summarization
Multi-document summarization (MDS) aims at producing a good-quality summary for several related documents.
S^4-Tuning: A Simple Cross-lingual Sub-network Tuning Method
The emergence of multilingual pre-trained language models makes it possible to adapt to target languages with only few labeled examples. However, vanilla fine-tuning tends to achieve degenerated and unstable results, owing to the Language Interference among different languages, and Parameter Overload under the few-sample transfer learning scenarios. To address two problems elegantly, we propose S^4-Tuning, a Simple Cross-lingual Sub-network Tuning method.
Focus on the Target’s Vocabulary: Masked Label Smoothing for Machine Translation
Label smoothing and vocabulary sharing are two widely used techniques in neural machine translation models.
基于双编码器的医学文本中文分词(Chinese word segmentation of medical text based on dual-encoder)
“中文分词是自然语言处理领域的基础工作, 然而前人的医学文本分词工作都只是直接套用通用分词的方法, 而医学文本多专用术语的特点让分词系统需要对医学专用术语和医学文本中的非医学术语文本提供不同的分词粒度。本文提出了双编码器医学文本中文分词模型, 利用辅助编码器为医学专有术语提供粗粒度表示。模型将需要粗粒度分词的医学专用术语和需要通用分词粒度的文本分开, 在提升医学专用术语的分词能力的同时最大限度地避免了其粗粒度对于医学文本中通用文本分词的干扰。”
生成,推理与排序:基于多任务架构的数学文字题生成(Generating, Reasoning & Ranking: Multitask Learning Framework for Math Word Problem Generation)
“数学文字题是一段能反映数学等式潜在逻辑的叙述性文本。成功的数学问题生成在语言生成和教育领域都具有广阔的应用前景。前人的工作大多需要人工标注的模板或关键词作为输入, 且未考虑数学表达式本身的特点。本文提出了一种多任务联合训练的问题文本生成模型。我们设计了三个辅助任务, 包括数字间关系抽取、数值排序和片段替换预测。他们与生成目标联合训练, 用以监督解码器的学习, 增强模型对运算逻辑和问题条件的感知能力。实验证明所提方法能有效提升生成的数学文字题的质量。”
融合知识的多目标词联合框架语义分析模型(Knowledge-integrated Joint Model For Multi-target Frame Semantic Parsing)
“框架语义分析任务是自然语言处理领域的一项基础性任务。先前的研究工作大多针对单目标词进行模型设计, 无法一次性完成多个目标词的框架语义结构提取。本文提出了一个面向多目标的框架语义分析模型, 实现对多目标词的联合预测。该模型对框架语义分析的各项子任务进行交互性建模, 实现子任务间的双向交互。此外, 本文利用关系图网络对框架关系信息进行编码, 将其作为框架语义学知识融入模型中。实验表明, 本文模型在不借助额外语料的情况下相比之前模型都有不同程度的提高。消融实验证明了本文模型设计的有效性。此外我们分析了模型目前存在的局限性以及未来的改进方向。”
Mitigating Language-Level Performance Disparity in mPLMs via Teacher Language Selection and Cross-lingual Self-Distillation
Therefore, we introduce ALSACE to leverage the learned knowledge from the well-performing languages to guide under-performing ones within the same mPLM, eliminating the need for additional labeled multilingual data.
An Image is Worth 1/2 Tokens After Layer 2: Plug-and-Play Inference Acceleration for Large Vision-Language Models
To this end, we introduce FastV, a versatile plug-and-play method designed to optimize computational efficiency by learning adaptive attention patterns in early layers and pruning visual tokens in subsequent ones.
Improving Event Definition Following For Zero-Shot Event Detection
We hypothesize that a diverse set of event types and definitions are the key for models to learn to follow event definitions while existing event extraction datasets focus on annotating many high-quality examples for a few event types.
PCA-Bench: Evaluating Multimodal Large Language Models in Perception-Cognition-Action Chain
To address this, we introduce Embodied-Instruction-Evolution (EIE), an automatic framework for synthesizing instruction tuning examples in multimodal embodied environments.
VeCAF: Vision-language Collaborative Active Finetuning with Training Objective Awareness
Finetuning a pretrained vision model (PVM) is a common technique for learning downstream vision tasks.
ML-Bench: Evaluating Large Language Models for Code Generation in Repository-Level Machine Learning Tasks
While Large Language Models (LLMs) have demonstrated proficiency in code generation benchmarks, translating these results into practical development scenarios - where leveraging existing repository-level libraries is the norm - remains challenging.
Distantly-Supervised Named Entity Recognition with Uncertainty-aware Teacher Learning and Student-student Collaborative Learning
Distantly-Supervised Named Entity Recognition (DS-NER) effectively alleviates the burden of annotation, but meanwhile suffers from the label noise.
Coarse-to-Fine Dual Encoders are Better Frame Identification Learners
Recent researches measure the similarity or matching score between targets and candidate frames by modeling frame definitions.
Guiding AMR Parsing with Reverse Graph Linearization
Abstract Meaning Representation (AMR) parsing aims to extract an abstract semantic graph from a given sentence.
Towards End-to-End Embodied Decision Making via Multi-modal Large Language Model: Explorations with GPT4-Vision and Beyond
In this study, we explore the potential of Multimodal Large Language Models (MLLMs) in improving embodied decision-making processes for agents.
MMICL: Empowering Vision-language Model with Multi-Modal In-Context Learning
In this paper, we address the limitation above by 1) introducing vision-language Model with Multi-Modal In-Context Learning(MMICL), a new approach to allow the VLM to deal with multi-modal inputs efficiently; 2) proposing a novel context scheme to augment the in-context learning ability of the VLM; 3) constructing the Multi-modal In-Context Learning (MIC) dataset, designed to enhance the VLM's ability to understand complex multi-modal prompts.
Ranked #16 on
Visual Reasoning
on Winoground
Mining Clues from Incomplete Utterance: A Query-enhanced Network for Incomplete Utterance Rewriting
Then, we adopt a fast and effective edit operation scoring network to model the relation between two tokens.
Human-in-the-Loop through Chain-of-Thought
While the emergence of powerful language models along with Chain-of-thought prompting has made automation more and more omnipresent, it sometimes demonstrates its weakness in long-term or multi-step logical reasoning.
DialogVCS: Robust Natural Language Understanding in Dialogue System Upgrade
In the constant updates of the product dialogue systems, we need to retrain the natural language understanding (NLU) model as new data from the real users would be merged into the existent data accumulated in the last updates.
Can We Edit Factual Knowledge by In-Context Learning?
Inspired by in-context learning (ICL), a new paradigm based on demonstration contexts without parameter updating, we explore whether ICL can edit factual knowledge.
DiffCap: Exploring Continuous Diffusion on Image Captioning
Our method transforms discrete tokens in a natural way and applies continuous diffusion on them to successfully fuse extracted image features for diffusion caption generation.
On the Off-Target Problem of Zero-Shot Multilingual Neural Machine Translation
We conduct experiments on a multilingual machine translation benchmark in 11 languages.
SANTA: Separate Strategies for Inaccurate and Incomplete Annotation Noise in Distantly-Supervised Named Entity Recognition
Distantly-Supervised Named Entity Recognition effectively alleviates the burden of time-consuming and expensive annotation in the supervised setting.
A Survey on In-context Learning
With the increasing ability of large language models (LLMs), in-context learning (ICL) has become a new paradigm for natural language processing (NLP), where LLMs make predictions only based on contexts augmented with a few examples.
Query Your Model with Definitions in FrameNet: An Effective Method for Frame Semantic Role Labeling
Such methods usually model role classification as naive multi-class classification and treat arguments individually, which neglects label semantics and interactions between arguments and thus hindering performance and generalization of models.
A Two-Stage Method for Chinese AMR Parsing
In this paper, we provide a detailed description of our system at CAMRP-2022 evaluation.
SCL-RAI: Span-based Contrastive Learning with Retrieval Augmented Inference for Unlabeled Entity Problem in NER
Named Entity Recognition is the task to locate and classify the entities in the text.
A Double-Graph Based Framework for Frame Semantic Parsing
In this paper, we propose a Knowledge-guided Incremental semantic parser with Double-graph (KID).
Robust Fine-tuning via Perturbation and Interpolation from In-batch Instances
For each instance in a batch, we involve other instances in the same batch to interact with it.
A Two-Stream AMR-enhanced Model for Document-level Event Argument Extraction
In this paper, we focus on extracting event arguments from an entire document, which mainly faces two critical problems: a) the long-distance dependency between trigger and arguments over sentences; b) the distracting context towards an event in the document.
Document-level Event Extraction
Event Argument Extraction
+2
ATP: AMRize Then Parse! Enhancing AMR Parsing with PseudoAMRs
As Abstract Meaning Representation (AMR) implicitly involves compound semantic annotations, we hypothesize auxiliary tasks which are semantically or formally related can better enhance AMR parsing.
Ranked #7 on
AMR Parsing
on LDC2020T02
(using extra training data)
StableMoE: Stable Routing Strategy for Mixture of Experts
We point out that existing learning-to-route MoE methods suffer from the routing fluctuation issue, i. e., the target expert of the same input may change along with training, but only one expert will be activated for the input during inference.
Mixture of Experts for Biomedical Question Answering
In this paper, in order to alleviate the parameter competition problem, we propose a Mixture-of-Expert (MoE) based question answering method called MoEBQA that decouples the computation for different types of questions by sparse routing.
DISK: Domain-constrained Instance Sketch for Math Word Problem Generation
A math word problem (MWP) is a coherent narrative which reflects the underlying logic of math equations.
A Roadmap for Big Model
With the rapid development of deep learning, training Big Models (BMs) for multiple downstream tasks becomes a popular paradigm.
Focus on the Target's Vocabulary: Masked Label Smoothing for Machine Translation
Label smoothing and vocabulary sharing are two widely used techniques in neural machine translation models.
From Dense to Sparse: Contrastive Pruning for Better Pre-trained Language Model Compression
Unified in contrastive learning, CAP enables the pruned model to learn from the pre-trained model for task-agnostic knowledge, and fine-tuned model for task-specific knowledge.
Hierarchical Curriculum Learning for AMR Parsing
Abstract Meaning Representation (AMR) parsing aims to translate sentences to semantic representation with a hierarchical structure, and is recently empowered by pretrained sequence-to-sequence models.
An Enhanced Span-based Decomposition Method for Few-Shot Sequence Labeling
Few-Shot Sequence Labeling (FSSL) is a canonical paradigm for the tagging models, e. g., named entity recognition and slot filling, to generalize on an emerging, resource-scarce domain.
Ranked #6 on
Few-shot NER
on Few-NERD (INTER)
Raise a Child in Large Language Model: Towards Effective and Generalizable Fine-tuning
Recent pretrained language models extend from millions to billions of parameters.
Behind the Scenes: An Exploration of Trigger Biases Problem in Few-Shot Event Classification
However, we find they suffer from trigger biases that signify the statistical homogeneity between some trigger words and target event types, which we summarize as trigger overlapping and trigger separability.
Explicit Interaction Network for Aspect Sentiment Triplet Extraction
Table encoder extracts sentiment at token-pair level, so that the compositional feature between targets and opinions can be easily captured.
CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark
Artificial Intelligence (AI), along with the recent progress in biomedical language understanding, is gradually changing medical practice.
Ranked #1 on
Semantic Similarity
on CHIP-STS
SIRE: Separate Intra- and Inter-sentential Reasoning for Document-level Relation Extraction
However, not all entity pairs can be connected with a path and have the correct logical reasoning paths in their graph.
Ranked #19 on
Relation Extraction
on DocRED
Decompose, Fuse and Generate: A Formation-Informed Method for Chinese Definition Generation
In this paper, we tackle the task of Definition Generation (DG) in Chinese, which aims at automatically generating a definition for a word.
Document-level Event Extraction via Heterogeneous Graph-based Interaction Model with a Tracker
Existing methods are not effective due to two challenges of this task: a) the target event arguments are scattered across sentences; b) the correlation among events in a document is non-trivial to model.
Ranked #2 on
Document-level Event Extraction
on ChFinAnn
Problems and Countermeasures in Natural Language Processing Evaluation
Starting from the concept, com-position, development and meaning of natural language evaluation, this article classifies and summarizes the tasks and char-acteristics of mainstream natural language evaluation, and then summarizes the problems and causes of natural language pro-cessing evaluation.
Knowledge Neurons in Pretrained Transformers
In this paper, we present preliminary studies on how factual knowledge is stored in pretrained Transformers by introducing the concept of knowledge neurons.
Incorporating Connections Beyond Knowledge Embeddings: A Plug-and-Play Module to Enhance Commonsense Reasoning in Machine Reading Comprehension
Conventional Machine Reading Comprehension (MRC) has been well-addressed by pattern matching, but the ability of commonsense reasoning remains a gap between humans and machines.
Towards Faithfulness in Open Domain Table-to-text Generation from an Entity-centric View
In open domain table-to-text generation, we notice that the unfaithful generation usually contains hallucinated content which can not be aligned to any input table record.
Generating Math Word Problems from Equations with Topic Controlling and Commonsense Enforcement
Recent years have seen significant advancement in text generation tasks with the help of neural language models.
Coarse-to-Fine Entity Representations for Document-level Relation Extraction
In classification, we combine the entity representations from both two levels into more comprehensive representations for relation extraction.
Ranked #34 on
Relation Extraction
on DocRED
An Anchor-Based Automatic Evaluation Metric for Document Summarization
The widespread adoption of reference-based automatic evaluation metrics such as ROUGE has promoted the development of document summarization.
Discriminatively-Tuned Generative Classifiers for Robust Natural Language Inference
We explore training objectives for discriminative fine-tuning of our generative classifiers, showing improvements over log loss fine-tuning from prior work .
An Empirical Study on Model-agnostic Debiasing Strategies for Robust Natural Language Inference
The prior work on natural language inference (NLI) debiasing mainly targets at one or few known biases while not necessarily making the models more robust.
Double Graph Based Reasoning for Document-level Relation Extraction
Document-level relation extraction aims to extract relations among entities within a document.
Ranked #12 on
Relation Extraction
on DocRED
Inductively Representing Out-of-Knowledge-Graph Entities by Optimal Estimation Under Translational Assumptions
Conventional Knowledge Graph Completion (KGC) assumes that all test entities appear during training.
HypoNLI: Exploring the Artificial Patterns of Hypothesis-only Bias in Natural Language Inference
Many recent studies have shown that for models trained on datasets for natural language inference (NLI), it is possible to make correct predictions by merely looking at the hypothesis while completely ignoring the premise.
Pun-GAN: Generative Adversarial Network for Pun Generation
It consists of a generator to produce pun sentences, and a discriminator to distinguish between the generated pun sentences and the real sentences with specific word senses.
Learning to Control the Fine-grained Sentiment for Story Ending Generation
Therefore, we propose a generic and novel framework which consists of a sentiment analyzer and a sentimental generator, respectively addressing the two challenges.
Towards Fine-grained Text Sentiment Transfer
In this paper, we focus on the task of fine-grained text sentiment transfer (FGST).
Towards Comprehensive Description Generation from Factual Attribute-value Tables
To relieve these problems, we first propose force attention (FA) method to encourage the generator to pay more attention to the uncovered attributes to avoid potential key attributes missing.
A Soft Label Strategy for Target-Level Sentiment Classification
In this paper, we propose a soft label approach to target-level sentiment classification task, in which a history-based soft labeling model is proposed to measure the possibility of a context word as an opinion word.
A Dual Reinforcement Learning Framework for Unsupervised Text Style Transfer
Therefore, in this paper, we propose a dual reinforcement learning framework to directly transfer the style of the text via a one-step mapping model, without any separation of content and style.
Ranked #1 on
Unsupervised Text Style Transfer
on GYAFC
Fine-grained Coordinated Cross-lingual Text Stream Alignment for Endless Language Knowledge Acquisition
This paper proposes to study fine-grained coordinated cross-lingual text stream alignment through a novel information network decipherment paradigm.
Leveraging Gloss Knowledge in Neural Word Sense Disambiguation by Hierarchical Co-Attention
The goal of Word Sense Disambiguation (WSD) is to identify the correct meaning of a word in the particular context.
Improved Dependency Parsing using Implicit Word Connections Learned from Unlabeled Data
Pre-trained word embeddings and language model have been shown useful in a lot of tasks.
Incorporating Glosses into Neural Word Sense Disambiguation
GAS models the semantic relationship between the context and the gloss in an improved memory network framework, which breaks the barriers of the previous supervised methods and knowledge-based methods.
Ranked #3 on
Word Sense Disambiguation
on SemEval 2015 Task 13
Table-to-text Generation by Structure-aware Seq2seq Learning
In the decoding phase, dual attention mechanism which contains word level attention and field level attention is proposed to model the semantic relevance between the generated description and the table.
Ranked #1 on
Table-to-Text Generation
on WikiBio
Order-Planning Neural Text Generation From Structured Data
Generating texts from structured data (e. g., a table) is important for various natural language processing tasks such as question answering and dialog systems.
Affinity-Preserving Random Walk for Multi-Document Summarization
Multi-document summarization provides users with a short text that summarizes the information in a set of related documents.
A Soft-label Method for Noise-tolerant Distantly Supervised Relation Extraction
Distant-supervised relation extraction inevitably suffers from wrong labeling problems because it heuristically labels relational facts with knowledge bases.
Syntax Aware LSTM model for Semantic Role Labeling
In Semantic Role Labeling (SRL) task, the tree structured dependency relation is rich in syntax information, but it is not well handled by existing models.
Gated Self-Matching Networks for Reading Comprehension and Question Answering
We first match the question and passage with gated attention-based recurrent networks to obtain the question-aware passage representation.
Ranked #35 on
Question Answering
on SQuAD1.1 dev
A Progressive Learning Approach to Chinese SRL Using Heterogeneous Data
But the training data of single corpus is often limited.
Syntax Aware LSTM Model for Chinese Semantic Role Labeling
As for semantic role labeling (SRL) task, when it comes to utilizing parsing information, both traditional methods and recent recurrent neural network (RNN) based methods use the feature engineering way.
Improving Chinese SRL with Heterogeneous Annotations
Previous studies on Chinese semantic role labeling (SRL) have concentrated on single semantically annotated corpus.
Towards Time-Aware Knowledge Graph Completion
In this paper, we present a novel time-aware knowledge graph completion model that is able to predict links in a KG using both the existing facts and the temporal information of the facts.
Event Detection with Burst Information Networks
Retrospective event detection is an important task for discovering previously unidentified events in a text stream.
Reading and Thinking: Re-read LSTM Unit for Textual Entailment Recognition
After read the premise again, the model can get a better understanding of the premise, which can also affect the understanding of the hypothesis.
Ranked #42 on
Natural Language Inference
on SNLI
Aligning Coordinated Text Streams through Burst Information Network Construction and Decipherment
We introduce a novel Burst Information Network (BINet) representation that can display the most important information and illustrate the connections among bursty entities, events and keywords in the corpus.
Learning Word Sense Embeddings from Word Sense Definitions
Word embeddings play a significant role in many modern NLP systems.
Joint Learning Templates and Slots for Event Schema Induction
Automatic event schema induction (AESI) means to extract meta-event from raw text, in other words, to find out what types (templates) of event may exist in the raw text and what roles (slots) may exist in each event type.
