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Found 67 papers, 34 papers with code
Leveraging Word-Formation Knowledge for Chinese Word Sense Disambiguation
In this paper, we propose to leverage word-formation knowledge to enhance Chinese WSD.
Large Language Models are not Fair Evaluators
We uncover a systematic bias in the evaluation paradigm of adopting large language models~(LLMs), e. g., GPT-4, as a referee to score the quality of responses generated by candidate models.
Denoising Bottleneck with Mutual Information Maximization for Video Multimodal Fusion
However, unlike other image-text multimodal tasks, video has longer multimodal sequences with more redundancy and noise in both visual and audio modalities.
Bi-Drop: Generalizable Fine-tuning for Pre-trained Language Models via Adaptive Subnetwork Optimization
Pretrained language models have achieved remarkable success in a variety of natural language understanding tasks.
Learning the String Partial Order
We show that most structured prediction problems can be solved in linear time and space by considering them as partial orderings of the tokens in the input string.
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.
Discourse Centric Evaluation of Machine Translation with a Densely Annotated Parallel Corpus
Several recent papers claim human parity at sentence-level Machine Translation (MT), especially in high-resource languages.
RepCL: Exploring Effective Representation for Continual Text Classification
Continual learning (CL) aims to constantly learn new knowledge over time while avoiding catastrophic forgetting on old tasks.
Enhancing Continual Relation Extraction via Classifier Decomposition
In this work, we point out that there exist two typical biases after training of this vanilla strategy: classifier bias and representation bias, which causes the previous knowledge that the model learned to be shaded.
DialogQAE: N-to-N Question Answer Pair Extraction from Customer Service Chatlog
Harvesting question-answer (QA) pairs from customer service chatlog in the wild is an efficient way to enrich the knowledge base for customer service chatbots in the cold start or continuous integration scenarios.
A Bilingual Parallel Corpus with Discourse Annotations
The BWB corpus consists of Chinese novels translated by experts into English, and the annotated test set is designed to probe the ability of machine translation systems to model various discourse phenomena.
Autoregressive Structured Prediction with Language Models
Recent years have seen a paradigm shift in NLP towards using pretrained language models ({PLM}) for a wide range of tasks.
Ranked #1 on
Relation Extraction
on CoNLL04
(RE+ Micro F1 metric)
DialogUSR: Complex Dialogue Utterance Splitting and Reformulation for Multiple Intent Detection
While interacting with chatbots, users may elicit multiple intents in a single dialogue utterance.
Streaming PAC-Bayes Gaussian process regression with a performance guarantee for online decision making
The framework offers both a guarantee of generalized performance and good accuracy.
Learning Robust Representations for Continual Relation Extraction via Adversarial Class Augmentation
In this paper, through empirical studies we argue that this assumption may not hold, and an important reason for catastrophic forgetting is that the learned representations do not have good robustness against the appearance of analogous relations in the subsequent learning process.
Less is More: Rethinking State-of-the-art Continual Relation Extraction Models with a Frustratingly Easy but Effective Approach
2) Balanced Tuning (BT) finetunes the model on the balanced memory data.
A Structured Span Selector
Many natural language processing tasks, e. g., coreference resolution and semantic role labeling, require selecting text spans and making decisions about them.
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
+1
HPT: Hierarchy-aware Prompt Tuning for Hierarchical Text Classification
However, in this paradigm, there exists a huge gap between the classification tasks with sophisticated label hierarchy and the masked language model (MLM) pretraining tasks of PLMs and thus the potentials of PLMs can not be fully tapped.
SmartSales: Sales Script Extraction and Analysis from Sales Chatlog
2) Customer objection response assists the salespeople to figure out the typical customer objections and corresponding winning sales scripts, as well as search for proper sales responses for a certain customer objection.
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 #6 on
AMR Parsing
on LDC2017T10
(using extra training data)
An alternative paradigm of fault diagnosis in dynamic systems: orthogonal projection-based methods
In this paper, we propose a new paradigm of fault diagnosis in dynamic systems as an alternative to the well-established observer-based framework.
Demystify Optimization and Generalization of Over-parameterized PAC-Bayesian Learning
In addition to being a pure generalization bound analysis tool, PAC-Bayesian bound can also be incorporated into an objective function to train a probabilistic neural network, making them a powerful and relevant framework that can numerically provide a tight generalization bound for supervised learning.
Semi-supervised Implicit Scene Completion from Sparse LiDAR
Recent advances show that semi-supervised implicit representation learning can be achieved through physical constraints like Eikonal equations.
Cerberus Transformer: Joint Semantic, Affordance and Attribute Parsing
Multi-task indoor scene understanding is widely considered as an intriguing formulation, as the affinity of different tasks may lead to improved performance.
Ranked #31 on
Semantic Segmentation
on NYU Depth v2
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 #2 on
Few-shot NER
on Few-NERD (INTER)
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.
Aspect Sentiment Triplet Extraction
Sentiment Classification
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
Premise-based Multimodal Reasoning: Conditional Inference on Joint Textual and Visual Clues
It is a common practice for recent works in vision language cross-modal reasoning to adopt a binary or multi-choice classification formulation taking as input a set of source image(s) and textual query.
Apply Artificial Neural Network to Solving Manpower Scheduling Problem
This paper proposes a new model combined with deep learning to solve the multi-shift manpower scheduling problem based on the existing research.
An Intelligent Model for Solving Manpower Scheduling Problems
In addition, to the numerical solution of the manpower scheduling problem, this paper also studies the algorithm for scheduling task list generation and the method of displaying scheduling results.
Automatic Learning to Detect Concept Drift
Many methods have been proposed to detect concept drift, i. e., the change in the distribution of streaming data, due to concept drift causes a decrease in the prediction accuracy of algorithms.
A Token-level Reference-free Hallucination Detection Benchmark for Free-form Text Generation
Large pretrained generative models like GPT-3 often suffer from hallucinating non-existent or incorrect content, which undermines their potential merits in real applications.
BlonDe: An Automatic Evaluation Metric for Document-level Machine Translation
Standard automatic metrics, e. g. BLEU, are not reliable for document-level MT evaluation.
Integrating Fast Regional Optimization into Sampling-based Kinodynamic Planning for Multirotor Flight
For real-time multirotor kinodynamic motion planning, the efficiency of sampling-based methods is usually hindered by difficult-to-sample homotopy classes like narrow passages.
Motion Planning
Robotics
Application of the unified control and detection framework to detecting stealthy integrity cyber-attacks on feedback control systems
It is demonstrated, in the unified framework of control and detection, that all kernel attacks can be structurally detected when not only the observer-based residual, but also the control signal based residual signals are generated and used for the detection purpose.
First Target and Opinion then Polarity: Enhancing Target-opinion Correlation for Aspect Sentiment Triplet Extraction
Aspect Sentiment Triplet Extraction (ASTE) aims to extract triplets from a sentence, including target entities, associated sentiment polarities, and opinion spans which rationalize the polarities.
Ranked #8 on
Aspect Sentiment Triplet Extraction
on ASTE-Data-V2
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.
PAC-Bayes Bounds for Meta-learning with Data-Dependent Prior
By leveraging experience from previous tasks, meta-learning algorithms can achieve effective fast adaptation ability when encountering new tasks.
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.
An Exploration of Arbitrary-Order Sequence Labeling via Energy-Based Inference Networks
Many tasks in natural language processing involve predicting structured outputs, e. g., sequence labeling, semantic role labeling, parsing, and machine translation.
Compare and Select: Video Summarization with Multi-Agent Reinforcement Learning
To solve the subjectivity problem, we study the general user summarization process.
The Role of the Hercules Autonomous Vehicle During the COVID-19 Pandemic: An Autonomous Logistic Vehicle for Contactless Goods Transportation
Moreover, many countries have imposed tough lockdown measures to reduce the virus transmission (e. g., retail, catering) during the pandemic, which causes inconveniences for human daily life.
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.
Table-to-Text Natural Language Generation with Unseen Schemas
In this paper, we propose the new task of table-to-text NLG with unseen schemas, which specifically aims to test the generalization of NLG for input tables with attribute types that never appear during training.
Key Fact as Pivot: A Two-Stage Model for Low Resource Table-to-Text Generation
We propose a novel model to separate the generation into two stages: key fact prediction and surface realization.
Towards Improving Neural Named Entity Recognition with Gazetteers
Most of the recently proposed neural models for named entity recognition have been purely data-driven, with a strong emphasis on getting rid of the efforts for collecting external resources or designing hand-crafted features.
Ranked #13 on
Named Entity Recognition (NER)
on Ontonotes v5 (English)
(using extra training data)
MAAM: A Morphology-Aware Alignment Model for Unsupervised Bilingual Lexicon Induction
The task of unsupervised bilingual lexicon induction (UBLI) aims to induce word translations from monolingual corpora in two languages.
Enhancing Topic-to-Essay Generation with External Commonsense Knowledge
Experiments show that with external commonsense knowledge and adversarial training, the generated essays are more novel, diverse, and topic-consistent than existing methods in terms of both automatic and human evaluation.
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 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 Novel Dual-Lidar Calibration Algorithm Using Planar Surfaces
Multiple lidars are prevalently used on mobile vehicles for rendering a broad view to enhance the performance of localization and perception systems.
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.
Phrase-level Self-Attention Networks for Universal Sentence Encoding
As a result, the memory consumption can be reduced because the self-attention is performed at the phrase level instead of the sentence level.
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
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.
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.
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.
