NeurlPS 2022 | 自然语言处理相关论文分类整理

  © 作者｜王晓磊

  机构｜中国人民大学高瓴人工智能学院 

 研究方向 | 对话式信息获取 

本文从NeurlPS 2022 的2000多篇接收论文中筛选出了与自然语言处理相关的论文200多篇，并按照研究主题进行分类整理，以供参考。

导读：

NeurIPS 2022 是 CCF A 类会议，人工智能领域方向的顶级国际会议之一。第36届神经信息处理系统会议将于今年 11 月 28 日至 12 月 9 日举行。官方发布的接收论文列表链接如下：https://nips.cc/Conferences/2022/Schedule?type=Poster。

本文从 2000 多篇接收论文中筛选出了与自然语言处理相关的论文 200 多篇，并按照研究主题进行分类整理，以供参考。论文列表也同步更新到 GitHub，欢迎大家关注和Star：github.com/RUCAIBox/Top-conference-paper-list。

目录:

• Model 【模型】

• Interpretability, Analysis and Evaluation 【可解释性、分析、评测】

• Robustness and Safety 【鲁棒性与安全】

• knowledge and reasoning 【知识与推理】

• Information Extraction 【信息抽取】

• Information Retrieval 【信息检索】

• Text Classification 【文本分类】

• Text Generation 【文本生成】

• Machine Translation and Multilinguality 【机器翻译与多语言】

• Multimodality 【多模态】

• Special Tasks 【特殊任务】

01

Model 

【模型】

1. Model Design 【模型设计】

• Recurrent Memory Transformer
  

• Jump Self-attention: Capturing High-order Statistics in Transformers

• Block-Recurrent Transformers

• Staircase Attention for Recurrent Processing of Sequences

• Non-Linguistic Supervision for Contrastive Learning of Sentence Embeddings

• Transcormer: Transformer for Sentence Scoring with Sliding Language Modeling

• Mixture-of-Experts with Expert Choice Routing

• On the Representation Collapse of Sparse Mixture of Experts

• Improving Transformer with an Admixture of Attention Heads

• Your Transformer May Not be as Powerful as You Expect

• Confident Adaptive Language Modeling

• Decoupled Context Processing for Context Augmented Language Modeling

• Unsupervised Cross-Task Generalization via Retrieval Augmentation

• Revisiting Neural Scaling Laws in Language and Vision

• Learning to Scaffold: Optimizing Model Explanations for Teaching

2. Model Compression 【模型压缩】

• Information-Theoretic Generative Model Compression with Variational Energy-based Model

• Towards Efficient Post-training Quantization of Pre-trained Language Models

• Outlier Suppression: Pushing the Limit of Low-bit Transformer Language Models

• Deep Compression of Pre-trained Transformer Models

• LiteTransformerSearch: Training-free On-device Search for Efficient Autoregressive Language Models

• GPT3.int8(): 8-bit Matrix Multiplication for Transformers at Scale

• MorphTE: Injecting Morphology in Tensorized Embeddings

• Few-shot Task-agnostic Neural Architecture Search for Distilling Large Language Models

• A Fast Post-Training Pruning Framework for Transformers

3. Model Training 【模型训练】

• Memorization Without Overfitting: Analyzing the Training Dynamics of Large Language Models

• Generating Training Data with Language Models: Towards Zero-Shot Language Understanding

• A Data-Augmentation Is Worth A Thousand Samples

• TokenMixup: Efficient Attention-guided Token-level Data Augmentation for Transformers

• The Stability-Efficiency Dilemma: Investigating Sequence Length Warmup for Training GPT Models

• Tempo: Accelerating Transformer-Based Model Training through Memory Footprint Reduction

• Training and Inference on Any-Order Autoregressive Models the Right Way

• Decentralized Training of Foundation Models in Heterogeneous Environment

4. Model Usage 【模型使用】

• The Unreliability of Explanations in Few-Shot In-Context Learning

• What Can Transformers Learn In-Context? A Case Study of Simple Function Classes

• Decoupling Knowledge from Memorization: Retrieval-augmented Prompt Learning

• Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning

• Training language models to follow instructions with human feedback

• LST: Ladder Side-Tuning for Parameter and Memory Efficient Transfer Learning

• How to talk to your model: Instructions, descriptions, and learning

• Data Distributional Properties Drive Emergent In-Context Learning in Transformers

• Sparse Structure Search for Parameter-Efficient Tuning

• Fine-Tuning Pre-Trained Language Models Effectively by Optimizing Subnetworks Adaptively

• Second Thoughts are Best: Learning to Re-Align With Human Values from Text Edits

• LIFT: Language-Interfaced FineTuning for Non-language Machine Learning Tasks

• Adapting to Domain Shift by Meta-Distillation from Mixture-of-Experts

  
  

02

Interpretability, Analysis and Evaluation 

【可解释性、分析、评测】

• CEBaB: Estimating the Causal Effects of Real-World Concepts on NLP Model Behavior

• Rule-Based but Flexible? Evaluating and Improving Language Models as Accounts of Human Moral Judgment

• Understanding the Failure of Batch Normalization for Transformers in NLP

• AttCAT: Explaining Transformers via Attentive Class Activation Tokens

• An empirical analysis of compute-optimal large language model training

• Why GANs are overkill for NLP

• Exploring Length Generalization in Large Language Models

• Capturing Failures of Large Language Models via Human Cognitive Biases

• Pre-Trained Model Reusability Evaluation for Small-Data Transfer Learning

• First is Better Than Last for Language Data Influence

• What are the best Systems? New Perspectives on NLP Benchmarking

• Characteristics of Harmful Text: Towards Rigorous Benchmarking of Language Models

• FETA: Towards Specializing Foundational Models for Expert Task Applications

• This is the way - lessons learned from designing and compiling LEPISZCZE, a comprehensive NLP benchmark for Polish

• Rethinking Knowledge Graph Evaluation Under the Open-World Assumption

• A Multi-Task Benchmark for Korean Legal Language Understanding and Judgement Prediction

03

Robustness and Safety 

【鲁棒性与安全】

• Active Learning Helps Pretrained Models Learn the Intended Task

• Improving Certified Robustness via Statistical Learning with Logical Reasoning

• Moderate-fitting as a Natural Backdoor Defender for Pre-trained Language Models

• BadPrompt: Backdoor Attacks on Continuous Prompts

• A Win-win Deal: Towards Sparse and Robust Pre-trained Language Models

• Exploring the Limits of Domain-Adaptive Training for Detoxifying Large-Scale Language Models

• AD-DROP: Attribution Driven Dropout for Robust Language Model Finetuning

• Large (robust) models from computational constraints

• Multitasking Models are Robust to Structural Failure: A Neural Model for Bilingual Cognitive Reserve

• A Unified Evaluation of Textual Backdoor Learning: Frameworks and Benchmarks

• Recovering Private Text in Federated Learning of Language Models

• LAMP: Extracting Text from Gradients with Language Model Priors

• SeqPATE: Differentially Private Text Generation via Knowledge Distillation

• Differentially Private Model Compression

• Federated Learning from Pre-Trained Models: A Contrastive Learning Approach

04

Knowledge and Reasoning 

【知识与推理】

• Learning to Sample and Aggregate: Few-shot Reasoning over Temporal Knowledge Graph

• Retaining Knowledge for Learning with Dynamic Definition

• Shadow Knowledge Distillation: Bridging Offline and Online Knowledge Transfer

• What Makes a "Good" Data Augmentation in Knowledge Distillation - A Statistical Perspective

• Learning to Reason with Neural Networks: Generalization, Unseen Data and Boolean Measures

• Roadblocks for Temporarily Disabling Shortcuts and Learning New Knowledge

• PALBERT: Teaching ALBERT to Ponder

• Locating and Editing Factual Associations in GPT

• OTKGE: Multi-modal Knowledge Graph Embeddings via Optimal Transport

• Large Language Models are Zero-Shot Reasoners

• STaR: Bootstrapping Reasoning With Reasoning

• Chain of Thought Prompting Elicits Reasoning in Large Language Models

• ELASTIC: Numerical Reasoning with Adaptive Symbolic Compiler

• Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering

• Inductive Logical Query Answering in Knowledge Graphs

• Formalizing Coherence and Consistency Applied to Transfer Learning in Neuro-Symbolic Autoencoders

• CoNSoLe: Convex Neural Symbolic Learning

• Deep Bidirectional Language-Knowledge Pretraining

• Neurosymbolic Deep Generative Models for Sequence Data with Relational Constraints

• Instance-based Learning for Knowledge Base Completion

• LogiGAN: Learning Logical Reasoning via Adversarial Pre-training

• Learning robust rule representations for abstract reasoning via internal inferences

• Solving Quantitative Reasoning Problems with Language Models

• Towards Better Evaluation for Dynamic Link Prediction

• Predictive Querying for Autoregressive Neural Sequence Models

• Semantic Probabilistic Layers for Neuro-Symbolic Learning

• End-to-end Symbolic Regression with Transformers

• A Unified Framework for Deep Symbolic Regression

• ZeroC: A Neuro-Symbolic Model for Zero-shot Concept Recognition and Acquisition at Inference Time

05

Information Extraction 

【信息抽取】

• Unifying Information Extraction with Latent Adaptive Structure-aware Generative Language Model

• TweetNERD - End to End Entity Linking Benchmark for Tweets

• METS-CoV: A Dataset of Medical Entity and Targeted Sentiment on COVID-19 Related Tweets

06

Information Retrieval

【信息检索】

• Transformer Memory as a Differentiable Search Index

• Autoregressive Search Engines: Generating Substrings as Document Identifiers

• A Neural Corpus Indexer for Document Retrieval

07

Text Classification 

【文本分类】

• CascadeXML: End-to-end Multi-Resolution Learning for Extreme Multi-Label Text Classification

• Text Classification with Born's Rule

• Public Wisdom Matters! Discourse-Aware Hyperbolic Fourier Co-Attention for Social Text Classification

08

Text Generation 

【文本生成】

• CoNT: Contrastive Neural Text Generation
  

• A Character-Level Length Control Algorithm for Non-Autoregressive Sentence Summarization

• Towards Improving Faithfulness in Abstractive Summarization

• QUARK: Controllable Text Generation with Reinforced Unlearning

• Teacher Forcing Recovers Reward Functions for Text Generation

• Retrieve, Reason, and Refine: Generating Accurate and Faithful Patient Instructions

• A Contrastive Framework for Neural Text Generation

• Learning to Break the Loop: Analyzing and Mitigating Repetitions for Neural Text Generation

• COLD Decoding: Energy-based Constrained Text Generation with Langevin Dynamics

• Diffusion-LM Improves Controllable Text Generation

• Factuality Enhanced Language Models for Open-Ended Text Generation

• Controllable Text Generation with Neurally-Decomposed Oracle

• InsNet: An Efficient, Flexible, and Performant Insertion-based Text Generation Model

• Relation-Constrained Decoding for Text Generation

• EHRSQL: A Practical Text-to-SQL Benchmark for Electronic Health Records

• TGEA 2.0: A Large-Scale Diagnostically Annotated Dataset with Benchmark Tasks for Text Generation of Pretrained Language Models

09

Machine Translation and Multilinguality 

【机器翻译与多语言】

• Exploring Non-Monotonic Latent Alignments for Non-Autoregressive Machine Translation

• A new dataset for multilingual keyphrase generation

• Less-forgetting Multi-lingual Fine-tuning

• Losses Can Be Blessings: Routing Self-Supervised Speech Representations Towards Efficient Multilingual and Multitask Speech Processing

• Refining Low-Resource Unsupervised Translation by Language Disentanglement of Multilingual Translation Model

• OccGen: Selection of Real-world Multilingual Parallel Data Balanced in Gender within Occupations

• Multilingual Abusive Comment Detection at Scale for Indic Languages

• The BigScience Corpus A 1.6TB Composite Multilingual Dataset

• Addressing Resource Scarcity across Sign Languages with Multilingual Pretraining and Unified-Vocabulary Datasets

10

Multimodality 

【多模态】

• REVIVE: Regional Visual Representation Matters in Knowledge-Based Visual Question Answering

• Mind the Gap: Understanding the Modality Gap in Multi-modal Contrastive Representation Learning

• GLIPv2: Unifying Localization and Vision-Language Understanding

• VLMo: Unified Vision-Language Pre-Training with Mixture-of-Modality-Experts

• A Differentiable Semantic Metric Approximation in Probabilistic Embedding for Cross-Modal Retrieval

• Egocentric Video-Language Pretraining

• Flamingo: a Visual Language Model for Few-Shot Learning

• Language Conditioned Spatial Relation Reasoning for 3D Object Grounding

• Multi-Granularity Cross-modal Alignment for Generalized Medical Visual Representation Learning

• Deep Multi-Modal Structural Equations For Causal Effect Estimation With Unstructured Proxies

• OmniVL: One Foundation Model for Image-Language and Video-Language Tasks

• Test-Time Prompt Tuning for Zero-Shot Generalization in Vision-Language Models

• Visual Clues: Bridging Vision and Language Foundations for Image Paragraph Captioning

• TVLT: Textless Vision-Language Transformer

• Divert More Attention to Vision-Language Tracking

• CogView2: Faster and Better Text-to-Image Generation via Hierarchical Transformers

• Text-Adaptive Multiple Visual Prototype Matching for Video-Text Retrieval

• BMU-MoCo: Bidirectional Momentum Update For Continual Video-Language Modeling

• Expectation-Maximization Contrastive Learning for Compact Video-and-Language Representations

• What is Where by Looking: Weakly-Supervised Open-World Phrase-Grounding without Text Inputs

• Flamingo: a Visual Language Model for Few-Shot Learning

• Self-Supervised Multi-Granularity Map Learning for Vision-and-Language Navigation

• UniCLIP: Unified Framework for Contrastive Language-Image Pre-training

• Contrastive Language-Image Pre-Training with Knowledge Graphs

• PyramidCLIP: Hierarchical Feature Alignment for Vision-language Model Pretraining

• Enhancing and Scaling Cross-Modality Alignment for Contrastive Multimodal Pre-Training via Gradient Harmonization

• Mutual Information Divergence: A Unified Metric for Multimodal Generative Models

• Transferring Pre-trained Multimodal Representations with Cross-modal Similarity Matching

• MACK: Multimodal Aligned Conceptual Knowledge for Unpaired Image-text Matching

• HUMANISE: Language-conditioned Human Motion Generation in 3D Scenes

• CyCLIP: Cyclic Contrastive Language-Image Pretraining

• S-Prompts Learning with Pre-trained Transformers: An Occam’s Razor for Domain Incremental Learning

• Delving into OOD Detection with Vision-Language Representations

• Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding

• Language Models with Image Descriptors are Strong Few-Shot Video-Language Learners

• DetCLIP: Dictionary-Enriched Visual-Concept Paralleled Pre-training for Open-world Detection

• Multimodal Contrastive Learning with LIMoE: the Language-Image Mixture of Experts

• Coarse-to-Fine Vision-Language Pre-training with Fusion in the Backbone

• CoupAlign: Coupling Word-Pixel with Sentence-Mask Alignments for Referring Image Segmentation

• Relational Language-Image Pre-training for Human-Object Interaction Detection

• Fine-Grained Semantically Aligned Vision-Language Pre-Training

• Cross-Linked Unified Embedding for cross-modality representation learning

• Quality Not Quantity: On the Interaction between Dataset Design and Robustness of CLIP

• Kernel Multimodal Continuous Attention

• Paraphrasing Is All You Need for Novel Object Captioning

• Long-Form Video-Language Pre-Training with Multimodal Temporal Contrastive Learning

• CLIPDraw: Exploring Text-to-Drawing Synthesis through Language-Image Encoders

• One Model to Edit Them All: Free-Form Text-Driven Image Manipulation with Semantic Modulations

• LGDN: Language-Guided Denoising Network for Video-Language Modeling

• Zero-Shot Video Question Answering via Frozen Bidirectional Language Models

• WinoGAViL: Gamified Association Benchmark to Challenge Vision-and-Language Models

• VLMbench: A Compositional Benchmark for Vision-and-Language Manipulation

• ELEVATER: A Benchmark and Toolkit for Evaluating Language-Augmented Visual Models

• LAION-5B: An open large-scale dataset for training next generation image-text models

• Towards Video Text Visual Question Answering: Benchmark and Baseline

• TaiSu: A 166M Large-scale High-Quality Dataset for Chinese Vision-Language Pre-training

• Wukong: A 100 Million Large-scale Chinese Cross-modal Pre-training Benchmark

• Understanding Aesthetics with Language: A Photo Critique Dataset for Aesthetic Assessment

• Multi-modal Robustness Analysis Against Language and Visual Perturbations

• CLiMB: A Continual Learning Benchmark for Vision-and-Language Tasks

• OrdinalCLIP: Learning Rank Prompts for Language-Guided Ordinal Regression

11

Special Tasks 

【特殊任务】

1. Code 【代码】

• CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning

• Fault-Aware Neural Code Rankers

• NS3: Neuro-symbolic Semantic Code Search

• Pyramid Attention For Source Code Summarization

2. Mathematics 【数学】

• HyperTree Proof Search for Neural Theorem Proving

• NaturalProver: Grounded Mathematical Proof Generation with Language Models

• Autoformalization with Large Language Models

• Thor: Wielding Hammers to Integrate Language Models and Automated Theorem Provers

3. Others 【其他】

• Measuring and Reducing Model Update Regression in Structured Prediction for NLP

• Learning to Follow Instructions in Text-Based Games

• WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agents

• LISA: Learning Interpretable Skill Abstractions from Language

• Inherently Explainable Reinforcement Learning in Natural Language

• Using natural language and program abstractions to instill human inductive biases in machines

• Semantic Exploration from Language Abstractions and Pretrained Representations

• Pre-Trained Language Models for Interactive Decision-Making

• Knowledge-Aware Bayesian Deep Topic Model

• Improving Intrinsic Exploration with Language Abstractions

• Improving Policy Learning via Language Dynamics Distillation

• Meta-Complementing the Semantics of Short Texts in Neural Topic Models

• Pile of Law: Learning Responsible Data Filtering from the Law and a 256GB Open-Source Legal Dataset

• BigBio: A Framework for Data-Centric Biomedical Natural Language Processing

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