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Found 61 papers, 37 papers with code
Phrase-aware Unsupervised Constituency Parsing
Recent studies have achieved inspiring success in unsupervised grammar induction using masked language modeling (MLM) as the proxy task.
Towards Adaptive Residual Network Training: A Neural-ODE Perspective
Serving as a crucial factor, the depth of residual networks balances model capacity, performance, and training efficiency.
META: Metadata-Empowered Weak Supervision for Text Classification
Based on seed words, we rank and filter motif instances to distill highly label-indicative ones as {``}seed motifs{''}, which provide additional weak supervision.
“Average” Approximates “First Principal Component”? An Empirical Analysis on Representations from Neural Language Models
In this paper, we present an empirical property of these representations—”average” approximates “first principal component”.
Learning Adaptive Axis Attentions in Fine-tuning: Beyond Fixed Sparse Attention Patterns
We present a comprehensive study of sparse attention patterns in Transformer models.
Towards Collaborative Neural-Symbolic Graph Semantic Parsing via Uncertainty
Recent work in task-independent graph semantic parsing has shifted from grammar-based symbolic approaches to neural models, showing strong performance on different types of meaning representations.
Federated Learning with Client-Exclusive Classes
In this paper, we aim to lift such an assumption and focus on a more general yet practical non-IID setting where every client can work on non-identical and even disjoint sets of classes (i. e., client-exclusive classes), and the clients have a common goal which is to build a global classification model to identify the union of these classes.
Modeling Label Semantics Improves Activity Recognition
We find that many activities in the current HAR datasets have shared label names, e. g., "open door" and "open fridge", "walk upstairs" and "walk downstairs".
MGDoc: Pre-training with Multi-granular Hierarchy for Document Image Understanding
In contrast, region-level models attempt to encode regions corresponding to paragraphs or text blocks into a single embedding, but they perform worse with additional word-level features.
Progressive Sentiment Analysis for Code-Switched Text Data
Instead of training on the entire code-switched corpus at once, we create buckets based on the fraction of words in the resource-rich language and progressively train from resource-rich language dominated samples to low-resource language dominated samples.
Waveformer: Linear-Time Attention with Forward and Backward Wavelet Transform
We propose Waveformer that learns attention mechanism in the wavelet coefficient space, requires only linear time complexity, and enjoys universal approximating power.
UCEpic: Unifying Aspect Planning and Lexical Constraints for Explainable Recommendation
In this paper, we propose UCEpic, an explanation generation model that unifies aspect planning and lexical constraints for controllable personalized generation.
SoTeacher: A Student-oriented Teacher Network Training Framework for Knowledge Distillation
To fill this gap, we propose a novel student-oriented teacher network training framework SoTeacher, inspired by recent findings that student performance hinges on teacher's capability to approximate the true label distribution of training samples.
LOPS: Learning Order Inspired Pseudo-Label Selection for Weakly Supervised Text Classification
Weakly supervised text classification methods typically train a deep neural classifier based on pseudo-labels.
Leveraging QA Datasets to Improve Generative Data Augmentation
The ability of generative language models (GLMs) to generate text has improved considerably in the last few years, enabling their use for generative data augmentation.
Fine-grained Contrastive Learning for Relation Extraction
Recent relation extraction (RE) works have shown encouraging improvements by conducting contrastive learning on silver labels generated by distant supervision before fine-tuning on gold labels.
Ranked #33 on
Relation Extraction
on DocRED
WeDef: Weakly Supervised Backdoor Defense for Text Classification
Inspired by this observation, in WeDef, we define the reliability of samples based on whether the predictions of the weak classifier agree with their labels in the poisoned training set.
Formulating Few-shot Fine-tuning Towards Language Model Pre-training: A Pilot Study on Named Entity Recognition
Fine-tuning pre-trained language models has recently become a common practice in building NLP models for various tasks, especially few-shot tasks.
OA-Mine: Open-World Attribute Mining for E-Commerce Products with Weak Supervision
Most prior works on this matter mine new values for a set of known attributes but cannot handle new attributes that arose from constantly changing data.
Towards Few-shot Entity Recognition in Document Images: A Label-aware Sequence-to-Sequence Framework
To overcome the data limitation, we propose to leverage the label surface names to better inform the model of the target entity type semantics and also embed the labels into the spatial embedding space to capture the spatial correspondence between regions and labels.
Label Noise in Adversarial Training: A Novel Perspective to Study Robust Overfitting
We show that label noise exists in adversarial training.
Perturbation Deterioration: The Other Side of Catastrophic Overfitting
While this phenomenon is commonly explained as overfitting, we observe that it is a twin process: not only does the model catastrophic overfits to one type of perturbation, but also the perturbation deteriorates into random noise.
Coarse2Fine: Fine-grained Text Classification on Coarsely-grained Annotated Data
Existing text classification methods mainly focus on a fixed label set, whereas many real-world applications require extending to new fine-grained classes as the number of samples per label increases.
BFClass: A Backdoor-free Text Classification Framework
To recognize the poisoned subset, we examine the training samples with these identified triggers as the most suspicious token, and check if removing the trigger will change the poisoned model's prediction.
LayoutReader: Pre-training of Text and Layout for Reading Order Detection
Reading order detection is the cornerstone to understanding visually-rich documents (e. g., receipts and forms).
Weakly Supervised Named Entity Tagging with Learnable Logical Rules
We study the problem of building entity tagging systems by using a few rules as weak supervision.
TaxoClass: Hierarchical Multi-Label Text Classification Using Only Class Names
Hierarchical multi-label text classification (HMTC) aims to tag each document with a set of classes from a taxonomic class hierarchy.
UCPhrase: Unsupervised Context-aware Quality Phrase Tagging
Training a conventional neural tagger based on silver labels usually faces the risk of overfitting phrase surface names.
Ranked #1 on
Phrase Tagging
on KPTimes
"Average" Approximates "First Principal Component"? An Empirical Analysis on Representations from Neural Language Models
In this paper, we present an empirical property of these representations -- "average" approximates "first principal component".
News Meets Microblog: Hashtag Annotation via Retriever-Generator
Hashtag annotation for microblog posts has been recently formulated as a sequence generation problem to handle emerging hashtags that are unseen in the training set.
Unsupervised Deep Keyphrase Generation
Keyphrase generation aims to summarize long documents with a collection of salient phrases.
Minimally-Supervised Structure-Rich Text Categorization via Learning on Text-Rich Networks
Specifically, we jointly train two modules with different inductive biases -- a text analysis module for text understanding and a network learning module for class-discriminative, scalable network learning.
Data Quality Matters For Adversarial Training: An Empirical Study
Specifically, we first propose a strategy to measure the data quality based on the learning behaviors of the data during adversarial training and find that low-quality data may not be useful and even detrimental to the adversarial robustness.
Sensei: Self-Supervised Sensor Name Segmentation
A sensor name, typically an alphanumeric string, encodes the key context (e. g., function and location) of a sensor needed for deploying smart building applications.
SeNsER: Learning Cross-Building Sensor Metadata Tagger
Sensor metadata tagging, akin to the named entity recognition task, provides key contextual information (e. g., measurement type and location) about sensors for running smart building applications.
X-Class: Text Classification with Extremely Weak Supervision
Finally, we pick the most confident documents from each cluster to train a text classifier.
Overfitting or Underfitting? Understand Robustness Drop in Adversarial Training
Our goal is to understand why the robustness drops after conducting adversarial training for too long.
SynSetExpan: An Iterative Framework for Joint Entity Set Expansion and Synonym Discovery
To facilitate the research on studying the interplays of these two tasks, we create the first large-scale Synonym-Enhanced Set Expansion (SE2) dataset via crowdsourcing.
Contextualized Weak Supervision for Text Classification
Weakly supervised text classification based on a few user-provided seed words has recently attracted much attention from researchers.
User-Guided Aspect Classification for Domain-Specific Texts
Aspect classification, identifying aspects of text segments, facilitates numerous applications, such as sentiment analysis and review summarization.
Empower Entity Set Expansion via Language Model Probing
Existing set expansion methods bootstrap the seed entity set by adaptively selecting context features and extracting new entities.
SetExpan: Corpus-Based Set Expansion via Context Feature Selection and Rank Ensemble
In this study, we propose a novel framework, SetExpan, which tackles this problem, with two techniques: (1) a context feature selection method that selects clean context features for calculating entity-entity distributional similarity, and (2) a ranking-based unsupervised ensemble method for expanding entity set based on denoised context features.
FUSE: Multi-Faceted Set Expansion by Coherent Clustering of Skip-grams
In this paper, we study the task of multi-faceted set expansion, which aims to capture all semantic facets in the seed set and return multiple sets of entities, one for each semantic facet.
CrossWeigh: Training Named Entity Tagger from Imperfect Annotations
Therefore, we manually correct these label mistakes and form a cleaner test set.
Ranked #3 on
Named Entity Recognition
on CoNLL++
(using extra training data)
Raw-to-End Name Entity Recognition in Social Media
Our model neither requires the conversion from character sequences to word sequences, nor assumes tokenizer can correctly detect all word boundaries.
Arabic Named Entity Recognition: What Works and What's Next
This paper presents the winning solution to the Arabic Named Entity Recognition challenge run by Topcoder. com.
Learning Named Entity Tagger using Domain-Specific Dictionary
Recent advances in deep neural models allow us to build reliable named entity recognition (NER) systems without handcrafting features.
Entity Set Search of Scientific Literature: An Unsupervised Ranking Approach
Different from Web or general domain search, a large portion of queries in scientific literature search are entity-set queries, that is, multiple entities of possibly different types.
Integrating Local Context and Global Cohesiveness for Open Information Extraction
However, current Open IE systems focus on modeling local context information in a sentence to extract relation tuples, while ignoring the fact that global statistics in a large corpus can be collectively leveraged to identify high-quality sentence-level extractions.
Efficient Contextualized Representation: Language Model Pruning for Sequence Labeling
Many efforts have been made to facilitate natural language processing tasks with pre-trained language models (LMs), and brought significant improvements to various applications.
Ranked #45 on
Named Entity Recognition
on CoNLL 2003 (English)
Investigating Rumor News Using Agreement-Aware Search
We build Maester based on the following two key observations: (1) relatedness can commonly be determined by keywords and entities occurring in both questions and articles, and (2) the level of agreement between the investigative question and the related news article can often be decided by a few key sentences.
Cross-type Biomedical Named Entity Recognition with Deep Multi-Task Learning
Motivation: State-of-the-art biomedical named entity recognition (BioNER) systems often require handcrafted features specific to each entity type, such as genes, chemicals and diseases.
An Attention-based Collaboration Framework for Multi-View Network Representation Learning
Existing approaches usually study networks with a single type of proximity between nodes, which defines a single view of a network.
Empower Sequence Labeling with Task-Aware Neural Language Model
In this study, we develop a novel neural framework to extract abundant knowledge hidden in raw texts to empower the sequence labeling task.
Ranked #13 on
Part-Of-Speech Tagging
on Penn Treebank
MetaPAD: Meta Pattern Discovery from Massive Text Corpora
We propose an efficient framework, called MetaPAD, which discovers meta patterns from massive corpora with three techniques: (1) it develops a context-aware segmentation method to carefully determine the boundaries of patterns with a learnt pattern quality assessment function, which avoids costly dependency parsing and generates high-quality patterns; (2) it identifies and groups synonymous meta patterns from multiple facets---their types, contexts, and extractions; and (3) it examines type distributions of entities in the instances extracted by each group of patterns, and looks for appropriate type levels to make discovered patterns precise.
Automated Phrase Mining from Massive Text Corpora
As one of the fundamental tasks in text analysis, phrase mining aims at extracting quality phrases from a text corpus.
DPPred: An Effective Prediction Framework with Concise Discriminative Patterns
In the literature, two series of models have been proposed to address prediction problems including classification and regression.
Meta-Path Guided Embedding for Similarity Search in Large-Scale Heterogeneous Information Networks
It models vertices as low-dimensional vectors to explore network structure-embedded similarity.
A Parallel and Efficient Algorithm for Learning to Match
In this paper, we tackle this challenge with a novel parallel and efficient algorithm for feature-based matrix factorization.
