Search Results for author:
Found 398 papers, 177 papers with code
LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding
Pre-training of text and layout has proved effective in a variety of visually-rich document understanding tasks due to its effective model architecture and the advantage of large-scale unlabeled scanned/digital-born documents.
Ranked #1 on
Key Information Extraction
on SROIE
Forging Multiple Training Objectives for Pre-trained Language Models via Meta-Learning
Multiple pre-training objectives fill the vacancy of the understanding capability of single-objective language modeling, which serves the ultimate purpose of pre-trained language models (PrLMs), generalizing well on a mass of scenarios.
Unsupervised Boundary-Aware Language Model Pretraining for Chinese Sequence Labeling
We apply BABERT for feature induction of Chinese sequence labeling tasks.
Ranked #1 on
Chinese Word Segmentation
on MSRA
Chinese Named Entity Recognition
Chinese Word Segmentation
+3
Eicient Non-Sampling Factorization Machines for Optimal Context-Aware Recommendation
Factorization Machines (FM) with negative sampling is a popular solution for context-aware recommendation.
Data Augmentation with Hierarchical SQL-to-Question Generation for Cross-domain Text-to-SQL Parsing
For better distribution matching, we require that at least 80% of SQL patterns in the training data are covered by generated queries.
R-Drop: Regularized Dropout for Neural Networks
Dropout is a powerful and widely used technique to regularize the training of deep neural networks.
Ranked #4 on
Machine Translation
on WMT2014 English-French
Efficient Second-Order TreeCRF for Neural Dependency Parsing
Experiments and analysis on 27 datasets from 13 languages clearly show that techniques developed before the DL era, such as structural learning (global TreeCRF loss) and high-order modeling are still useful, and can further boost parsing performance over the state-of-the-art biaffine parser, especially for partially annotated training data.
Ranked #1 on
Dependency Parsing
on CoNLL-2009
Improving the Transformer Translation Model with Document-Level Context
Although the Transformer translation model (Vaswani et al., 2017) has achieved state-of-the-art performance in a variety of translation tasks, how to use document-level context to deal with discourse phenomena problematic for Transformer still remains a challenge.
MuCGEC: a Multi-Reference Multi-Source Evaluation Dataset for Chinese Grammatical Error Correction
This paper presents MuCGEC, a multi-reference multi-source evaluation dataset for Chinese Grammatical Error Correction (CGEC), consisting of 7, 063 sentences collected from three Chinese-as-a-Second-Language (CSL) learner sources.
Chinese Sequence Labeling with Semi-Supervised Boundary-Aware Language Model Pre-training
Experimental results on Chinese sequence labeling datasets demonstrate that the improved BABERT variant outperforms the vanilla version, not only on these tasks but also more broadly across a range of Chinese natural language understanding tasks.
Marabou 2.0: A Versatile Formal Analyzer of Neural Networks
This paper serves as a comprehensive system description of version 2. 0 of the Marabou framework for formal analysis of neural networks.
Efficient Document-level Event Extraction via Pseudo-Trigger-aware Pruned Complete Graph
Most previous studies of document-level event extraction mainly focus on building argument chains in an autoregressive way, which achieves a certain success but is inefficient in both training and inference.
Ranked #3 on
Document-level Event Extraction
on ChFinAnn
A Large-Scale Rich Context Query and Recommendation Dataset in Online Knowledge-Sharing
To the best of our knowledge, this is the largest real-world interaction dataset for personalized recommendation.
Cobra: Extending Mamba to Multi-Modal Large Language Model for Efficient Inference
In recent years, the application of multimodal large language models (MLLM) in various fields has achieved remarkable success.
A Survey on Non-Autoregressive Generation for Neural Machine Translation and Beyond
While NAR generation can significantly accelerate inference speed for machine translation, the speedup comes at the cost of sacrificed translation accuracy compared to its counterpart, autoregressive (AR) generation.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+11
Distantly Supervised NER with Partial Annotation Learning and Reinforcement Learning
A bottleneck problem with Chinese named entity recognition (NER) in new domains is the lack of annotated data.
Chinese Named Entity Recognition
named-entity-recognition
+5
Learning To Retrieve: How to Train a Dense Retrieval Model Effectively and Efficiently
Through this process, it teaches the DR model how to retrieve relevant documents from the entire corpus instead of how to rerank a potentially biased sample of documents.
Optimizing Dense Retrieval Model Training with Hard Negatives
ADORE replaces the widely-adopted static hard negative sampling method with a dynamic one to directly optimize the ranking performance.
Jointly Optimizing Query Encoder and Product Quantization to Improve Retrieval Performance
Compared with previous DR models that use brute-force search, JPQ almost matches the best retrieval performance with 30x compression on index size.
Learning Discrete Representations via Constrained Clustering for Effective and Efficient Dense Retrieval
However, the efficiency of most existing DR models is limited by the large memory cost of storing dense vectors and the time-consuming nearest neighbor search (NNS) in vector space.
A Survey of Large Language Models Attribution
Open-domain generative systems have gained significant attention in the field of conversational AI (e. g., generative search engines).
RepBERT: Contextualized Text Embeddings for First-Stage Retrieval
Although exact term match between queries and documents is the dominant method to perform first-stage retrieval, we propose a different approach, called RepBERT, to represent documents and queries with fixed-length contextualized embeddings.
OpenBA: An Open-sourced 15B Bilingual Asymmetric seq2seq Model Pre-trained from Scratch
This report provides the main details to pre-train an analogous model, including pre-training data processing, Bilingual Flan data collection, the empirical observations that inspire our model architecture design, training objectives of different stages, and other enhancement techniques.
IPRE: a Dataset for Inter-Personal Relationship Extraction
Our data is the first dataset for inter-personal relationship extraction.
Mirror: A Universal Framework for Various Information Extraction Tasks
Sharing knowledge between information extraction tasks has always been a challenge due to the diverse data formats and task variations.
Modeling Graph Structure in Transformer for Better AMR-to-Text Generation
Recent studies on AMR-to-text generation often formalize the task as a sequence-to-sequence (seq2seq) learning problem by converting an Abstract Meaning Representation (AMR) graph into a word sequence.
SynGEC: Syntax-Enhanced Grammatical Error Correction with a Tailored GEC-Oriented Parser
Then, we obtain parse trees of the source incorrect sentences by projecting trees of the target correct sentences.
Towards Making the Most of ChatGPT for Machine Translation
We show that: 1) The performance of ChatGPT depends largely on temperature, and a lower temperature usually can achieve better performance; 2) Emphasizing the task information can further improve ChatGPT's performance, particularly in complex MT tasks; 3) Introducing domain information can elicit ChatGPT's generalization ability and improve its performance in the specific domain; 4) ChatGPT tends to generate hallucinations for non-English-centric MT tasks, which can be partially addressed by our proposed prompts but still need to be highlighted for the MT/NLP community.
NaSGEC: a Multi-Domain Chinese Grammatical Error Correction Dataset from Native Speaker Texts
We introduce NaSGEC, a new dataset to facilitate research on Chinese grammatical error correction (CGEC) for native speaker texts from multiple domains.
Towards Representation Alignment and Uniformity in Collaborative Filtering
Then, we empirically analyze the learning dynamics of typical CF methods in terms of quantified alignment and uniformity, which shows that better alignment or uniformity both contribute to higher recommendation performance.
Jointly Learning Explainable Rules for Recommendation with Knowledge Graph
The framework encourages two modules to complement each other in generating effective and explainable recommendation: 1) inductive rules, mined from item-centric knowledge graphs, summarize common multi-hop relational patterns for inferring different item associations and provide human-readable explanation for model prediction; 2) recommendation module can be augmented by induced rules and thus have better generalization ability dealing with the cold-start issue.
Semantic Role Labeling as Dependency Parsing: Exploring Latent Tree Structures Inside Arguments
Semantic role labeling (SRL) is a fundamental yet challenging task in the NLP community.
Dependency Parsing
Semantic Role Labeling (predicted predicates)
A new dataset of dog breed images and a benchmark for fine-grained classification
It is currently the largest dataset for fine-grained classification of dogs, including130 dog breeds and 70, 428 real-world images.
Variational Neural Machine Translation
Models of neural machine translation are often from a discriminative family of encoderdecoders that learn a conditional distribution of a target sentence given a source sentence.
Disentangled Modeling of Domain and Relevance for Adaptable Dense Retrieval
By making the REM and DAMs disentangled, DDR enables a flexible training paradigm in which REM is trained with supervision once and DAMs are trained with unsupervised data.
Neural Logic Reasoning
Both reasoning and generalization ability are important for prediction tasks such as recommender systems, where reasoning provides strong connection between user history and target items for accurate prediction, and generalization helps the model to draw a robust user portrait over noisy inputs.
CoachLM: Automatic Instruction Revisions Improve the Data Quality in LLM Instruction Tuning
Instruction tuning is crucial for enabling Language Learning Models (LLMs) in responding to human instructions.
CCKS 2019 Shared Task on Inter-Personal Relationship Extraction
In this paper, we present the task definition, the description of data and the evaluation methodology used during this shared task.
LMEye: An Interactive Perception Network for Large Language Models
LMEye addresses this issue by allowing the LLM to request the desired visual information aligned with various human instructions, which we term as the dynamic visual information interaction.
LasUIE: Unifying Information Extraction with Latent Adaptive Structure-aware Generative Language Model
Universally modeling all typical information extraction tasks (UIE) with one generative language model (GLM) has revealed great potential by the latest study, where various IE predictions are unified into a linearized hierarchical expression under a GLM.
Improving AMR Parsing with Sequence-to-Sequence Pre-training
In the literature, the research on abstract meaning representation (AMR) parsing is much restricted by the size of human-curated dataset which is critical to build an AMR parser with good performance.
Ranked #15 on
AMR Parsing
on LDC2017T10
(using extra training data)
Contrastive Attention Mechanism for Abstractive Sentence Summarization
We propose a contrastive attention mechanism to extend the sequence-to-sequence framework for abstractive sentence summarization task, which aims to generate a brief summary of a given source sentence.
EasyRL4Rec: An Easy-to-use Library for Reinforcement Learning Based Recommender Systems
EasyRL4Rec seeks to facilitate the model development and experimental process in the domain of RL-based RSs.
RaSa: Relation and Sensitivity Aware Representation Learning for Text-based Person Search
RA offsets the overfitting risk by introducing a novel positive relation detection task (i. e., learning to distinguish strong and weak positive pairs).
Ranked #2 on
Text based Person Retrieval
on RSTPReid
R-PointHop: A Green, Accurate, and Unsupervised Point Cloud Registration Method
Inspired by the recent PointHop classification method, an unsupervised 3D point cloud registration method, called R-PointHop, is proposed in this work.
Recommendation Unlearning
From the perspective of utility, if a system's utility is damaged by some bad data, the system needs to forget these data to regain utility.
Joint Multi-modal Aspect-Sentiment Analysis with Auxiliary Cross-modal Relation Detection
Therefore, in this paper, we are the first to jointly perform multi-modal ATE (MATE) and multi-modal ASC (MASC), and we propose a multi-modal joint learning approach with auxiliary cross-modal relation detection for multi-modal aspect-level sentiment analysis (MALSA).
An Empirical Study of CLIP for Text-based Person Search
TPBS, as a fine-grained cross-modal retrieval task, is also facing the rise of research on the CLIP-based TBPS.
Ranked #4 on
Text based Person Retrieval
on RSTPReid
Can Diffusion Model Achieve Better Performance in Text Generation? Bridging the Gap between Training and Inference!
Diffusion models have been successfully adapted to text generation tasks by mapping the discrete text into the continuous space.
Towards Accurate and Consistent Evaluation: A Dataset for Distantly-Supervised Relation Extraction
The experimental results show that the ranking lists of the comparison systems on the DS-labelled test data and human-annotated test data are different.
Improving Seq2Seq Grammatical Error Correction via Decoding Interventions
In this paper, we propose a unified decoding intervention framework that employs an external critic to assess the appropriateness of the token to be generated incrementally, and then dynamically influence the choice of the next token.
Ranked #1 on
Grammatical Error Correction
on MuCGEC
Zero-Shot Cross-Lingual Abstractive Sentence Summarization through Teaching Generation and Attention
But there is no cross-lingual parallel corpus, whose source sentence language is different to the summary language, to directly train a cross-lingual ASSUM system.
SelfMix: Robust Learning Against Textual Label Noise with Self-Mixup Training
The conventional success of textual classification relies on annotated data, and the new paradigm of pre-trained language models (PLMs) still requires a few labeled data for downstream tasks.
Jointly Non-Sampling Learning for Knowledge Graph Enhanced Recommendation
However, existing KG enhanced recommendation methods have largely focused on exploring advanced neural network architectures to better investigate the structural information of KG.
Robust Self-Augmentation for Named Entity Recognition with Meta Reweighting
Prior research has mainly resorted to heuristic rule-based constraints to reduce the noise for specific self-augmentation methods individually.
ODE Transformer: An Ordinary Differential Equation-Inspired Model for Sequence Generation
Inspired by this, we design a new architecture, {\it ODE Transformer}, which is analogous to the Runge-Kutta method that is well motivated in ODE.
TSRankLLM: A Two-Stage Adaptation of LLMs for Text Ranking
Text ranking is a critical task in various information retrieval applications, and the recent success of pre-trained language models (PLMs), especially large language models (LLMs), has sparked interest in their application to text ranking.
Rethinking Negative Instances for Generative Named Entity Recognition
In the Named Entity Recognition (NER) task, recent advancements have seen the remarkable improvement of LLMs in a broad range of entity domains via instruction tuning, by adopting entity-centric schema.
Code Summarization with Structure-induced Transformer
Code summarization (CS) is becoming a promising area in recent language understanding, which aims to generate sensible human language automatically for programming language in the format of source code, serving in the most convenience of programmer developing.
Stochastic Variance Reduced Ensemble Adversarial Attack for Boosting the Adversarial Transferability
The black-box adversarial attack has attracted impressive attention for its practical use in the field of deep learning security.
Visual Spatial Description: Controlled Spatial-Oriented Image-to-Text Generation
Accordingly, we manually annotate a dataset to facilitate the investigation of the newly-introduced task and build several benchmark encoder-decoder models by using VL-BART and VL-T5 as backbones.
Generating Visual Spatial Description via Holistic 3D Scene Understanding
With an external 3D scene extractor, we obtain the 3D objects and scene features for input images, based on which we construct a target object-centered 3D spatial scene graph (Go3D-S2G), such that we model the spatial semantics of target objects within the holistic 3D scenes.
Collaborative Word-based Pre-trained Item Representation for Transferable Recommendation
In this paper, we propose CoWPiRec, an approach of Collaborative Word-based Pre-trained item representation for Recommendation.
PointHop: An Explainable Machine Learning Method for Point Cloud Classification
In the attribute building stage, we address the problem of unordered point cloud data using a space partitioning procedure and developing a robust descriptor that characterizes the relationship between a point and its one-hop neighbor in a PointHop unit.
Breaking the Representation Bottleneck of Chinese Characters: Neural Machine Translation with Stroke Sequence Modeling
Existing research generally treats Chinese character as a minimum unit for representation.
Ranked #1 on
Machine Translation
on WMT2017 Chinese-English
A Structure Self-Aware Model for Discourse Parsing on Multi-Party Dialogues
Conversational discourse structures aim to describe how a dialogue is organised, thus they are helpful for dialogue understanding and response generation.
Ranked #3 on
Discourse Parsing
on STAC
Revisiting Grammatical Error Correction Evaluation and Beyond
Pretraining-based (PT-based) automatic evaluation metrics (e. g., BERTScore and BARTScore) have been widely used in several sentence generation tasks (e. g., machine translation and text summarization) due to their better correlation with human judgments over traditional overlap-based methods.
Generative Multimodal Entity Linking
Multimodal Entity Linking (MEL) is the task of mapping mentions with multimodal contexts to the referent entities from a knowledge base.
Intent-aware Ranking Ensemble for Personalized Recommendation
To address such a task, we propose an Intent-aware ranking Ensemble Learning~(IntEL) model to fuse multiple single-objective item lists with various user intents, in which item-level personalized weights are learned.
Variational Neural Discourse Relation Recognizer
In order to perform efficient inference and learning, we introduce neural discourse relation models to approximate the prior and posterior distributions of the latent variable, and employ these approximated distributions to optimize a reparameterized variational lower bound.
Syntax-aware Neural Semantic Role Labeling
Semantic role labeling (SRL), also known as shallow semantic parsing, is an important yet challenging task in NLP.
Fast and Accurate End-to-End Span-based Semantic Role Labeling as Word-based Graph Parsing
Moreover, we propose a simple constrained Viterbi procedure to ensure the legality of the output graph according to the constraints of the SRL structure.
ConsistTL: Modeling Consistency in Transfer Learning for Low-Resource Neural Machine Translation
In this paper, we propose a novel transfer learning method for NMT, namely ConsistTL, which can continuously transfer knowledge from the parent model during the training of the child model.
Prompt-based Distribution Alignment for Unsupervised Domain Adaptation
Therefore, in this paper, we first experimentally demonstrate that the unsupervised-trained VLMs can significantly reduce the distribution discrepancy between source and target domains, thereby improving the performance of UDA.
Expressive Forecasting of 3D Whole-body Human Motions
Human motion forecasting, with the goal of estimating future human behavior over a period of time, is a fundamental task in many real-world applications.
A User-Centric Benchmark for Evaluating Large Language Models
To address this oversight, we propose benchmarking LLMs from a user perspective in both dataset construction and evaluation designs.
PointHop++: A Lightweight Learning Model on Point Sets for 3D Classification
The PointHop method was recently proposed by Zhang et al. for 3D point cloud classification with unsupervised feature extraction.
Bilingual Dictionary Based Neural Machine Translation without Using Parallel Sentences
In this paper, we propose a new task of machine translation (MT), which is based on no parallel sentences but can refer to a ground-truth bilingual dictionary.
A Unified Span-Based Approach for Opinion Mining with Syntactic Constituents
Fine-grained opinion mining (OM) has achieved increasing attraction in the natural language processing (NLP) community, which aims to find the opinion structures of {``}Who expressed what opinions towards what{''} in one sentence.
Semi-supervised Domain Adaptation for Dependency Parsing
During the past decades, due to the lack of sufficient labeled data, most studies on cross-domain parsing focus on unsupervised domain adaptation, assuming there is no target-domain training data.
A Syntax-aware Multi-task Learning Framework for Chinese Semantic Role Labeling
In this paper, we adopt a simple unified span-based model for both span-based and word-based Chinese SRL as a strong baseline.
Train Your Data Processor: Distribution-Aware and Error-Compensation Coordinate Decoding for Human Pose Estimation
Recently, the leading performance of human pose estimation is dominated by heatmap based methods.
Multi-Agent Collaboration Framework for Recommender Systems
LLM-based agents have gained considerable attention for their decision-making skills and ability to handle complex tasks.
Combining Static Word Embeddings and Contextual Representations for Bilingual Lexicon Induction
Bilingual Lexicon Induction (BLI) aims to map words in one language to their translations in another, and is typically through learning linear projections to align monolingual word representation spaces.
Towards Unifying the Label Space for Aspect- and Sentence-based Sentiment Analysis
The aspect-based sentiment analysis (ABSA) is a fine-grained task that aims to determine the sentiment polarity towards targeted aspect terms occurring in the sentence.
Ranked #4 on
Aspect-Based Sentiment Analysis (ABSA)
on SemEval-2014 Task-4
(using extra training data)
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+3
A Multi-Modal Context Reasoning Approach for Conditional Inference on Joint Textual and Visual Clues
This makes the language model well-suitable for such multi-modal reasoning scenario on joint textual and visual clues.
Language Models are Universal Embedders
As such cases span from English to other natural or programming languages, from retrieval to classification and beyond, it is desirable to build a unified embedding model rather than dedicated ones for each scenario.
An In-depth Study on Internal Structure of Chinese Words
Several previous works on syntactic parsing propose to annotate shallow word-internal structures for better utilizing character-level information.
RST Discourse Parsing with Second-Stage EDU-Level Pre-training
Pre-trained language models (PLMs) have shown great potentials in natural language processing (NLP) including rhetorical structure theory (RST) discourse parsing. Current PLMs are obtained by sentence-level pre-training, which is different from the basic processing unit, i. e. element discourse unit (EDU). To this end, we propose a second-stage EDU-level pre-training approach in this work, which presents two novel tasks to learn effective EDU representations continually based on well pre-trained language models. Concretely, the two tasks are (1) next EDU prediction (NEP) and (2) discourse marker prediction (DMP). We take a state-of-the-art transition-based neural parser as baseline, and adopt it with a light bi-gram EDU modification to effectively explore the EDU-level pre-trained EDU representation. Experimental results on a benckmark dataset show that our method is highly effective, leading a 2. 1-point improvement in F1-score. All codes and pre-trained models will be released publicly to facilitate future studies.
Brain Topography Adaptive Network for Satisfaction Modeling in Interactive Information Access System
We explore the effectiveness of BTA for satisfaction modeling in two popular information access scenarios, i. e., search and recommendation.
Improving Temporal Generalization of Pre-trained Language Models with Lexical Semantic Change
Existing methods mainly perform continual training to mitigate such a misalignment.
Large Language Model Cascades with Mixture of Thoughts Representations for Cost-efficient Reasoning
Large language models (LLMs) such as GPT-4 have exhibited remarkable performance in a variety of tasks, but this strong performance often comes with the high expense of using paid API services.
Towards Better Understanding of User Satisfaction in Open-Domain Conversational Search
This raises the question of how to accurately model user satisfaction in conversational search scenarios.
Improving Simultaneous Machine Translation with Monolingual Data
Preliminary experiments on En-Zh and En-Ja news domain corpora demonstrate that monolingual data can significantly improve translation quality (e. g., +3. 15 BLEU on En-Zh).
Fuzzy Alignments in Directed Acyclic Graph for Non-Autoregressive Machine Translation
Non-autoregressive translation (NAT) reduces the decoding latency but suffers from performance degradation due to the multi-modality problem.
Interpretable Convolutional Neural Networks via Feedforward Design
To construct convolutional layers, we develop a new signal transform, called the Saab (Subspace Approximation with Adjusted Bias) transform.
Self-Correlation and Cross-Correlation Learning for Few-Shot Remote Sensing Image Semantic Segmentation
To tackle these challenges, we propose a Self-Correlation and Cross-Correlation Learning Network for the few-shot remote sensing image semantic segmentation.
SiLLM: Large Language Models for Simultaneous Machine Translation
We propose SiLLM, which delegates the two sub-tasks to separate agents, thereby incorporating LLM into SiMT.
Attributes-Guided and Pure-Visual Attention Alignment for Few-Shot Recognition
However, these approaches only augment the representations of samples with available semantics while ignoring the query set, which loses the potential for the improvement and may lead to a shift between the modalities combination and the pure-visual representation.
CT4Rec: Simple yet Effective Consistency Training for Sequential Recommendation
State-of-the-art sequential recommendation models proposed very recently combine contrastive learning techniques for obtaining high-quality user representations.
Tree Structure-Aware Few-Shot Image Classification via Hierarchical Aggregation
To solve this problem, we present a plug-in Hierarchical Tree Structure-aware (HTS) method, which not only learns the relationship of FSL and pretext tasks, but more importantly, can adaptively select and aggregate feature representations generated by pretext tasks to maximize the performance of FSL tasks.
Enhancing Multi-modal and Multi-hop Question Answering via Structured Knowledge and Unified Retrieval-Generation
Moreover, the pipelined approaches of retrieval and generation might result in poor generation performance when retrieval performance is low.
Troika: Multi-Path Cross-Modal Traction for Compositional Zero-Shot Learning
Recent compositional zero-shot learning (CZSL) methods adapt pre-trained vision-language models (VLMs) by constructing trainable prompts only for composed state-object pairs.
CMD: a framework for Context-aware Model self-Detoxification
In view of this, we introduce a Context-aware Model self-Detoxification~(CMD) framework that pays attention to both the context and the detoxification process, i. e., first detoxifying the context and then making the language model generate along the safe context.
Learning Semantic-Aligned Feature Representation for Text-based Person Search
In this paper, we propose a semantic-aligned embedding method for text-based person search, in which the feature alignment across modalities is achieved by automatically learning the semantic-aligned visual features and textual features.
Ranked #9 on
Text based Person Retrieval
on CUHK-PEDES
Evaluating Interpolation and Extrapolation Performance of Neural Retrieval Models
For example, representation-based retrieval models perform almost as well as interaction-based retrieval models in terms of interpolation but not extrapolation.
Speaker-Aware Discourse Parsing on Multi-Party Dialogues
It is believed that speaker interactions are helpful for this task.
Ranked #2 on
Discourse Parsing
on STAC
Bridging the Domain Gaps in Context Representations for k-Nearest Neighbor Neural Machine Translation
$k$-Nearest neighbor machine translation ($k$NN-MT) has attracted increasing attention due to its ability to non-parametrically adapt to new translation domains.
Non-autoregressive Streaming Transformer for Simultaneous Translation
Simultaneous machine translation (SiMT) models are trained to strike a balance between latency and translation quality.
Towards Reasoning in Large Language Models via Multi-Agent Peer Review Collaboration
Large Language Models (LLMs) have shown remarkable capabilities in general natural language processing tasks but often fall short in complex reasoning tasks.
SelectIT: Selective Instruction Tuning for Large Language Models via Uncertainty-Aware Self-Reflection
In this work, we propose a novel approach, termed SelectIT, that capitalizes on the foundational capabilities of the LLM itself.
Instruction-Driven Game Engines on Large Language Models
The Instruction-Driven Game Engine (IDGE) project aims to democratize game development by enabling a large language model (LLM) to follow free-form game rules and autonomously generate game-play processes.
Toward Adversarial Training on Contextualized Language Representation
Based on the observation, we propose simple yet effective \textit{Contextualized representation-Adversarial Training} (CreAT), in which the attack is explicitly optimized to deviate the contextualized representation of the encoder.
Sequential Recommendation with Latent Relations based on Large Language Model
Different from previous relation-aware models that rely on predefined rules, we propose to leverage the Large Language Model (LLM) to provide new types of relations and connections between items.
Code-Switching for Enhancing NMT with Pre-Specified Translation
Leveraging user-provided translation to constrain NMT has practical significance.
A Label Dependence-aware Sequence Generation Model for Multi-level Implicit Discourse Relation Recognition
Then, we employ a label sequence decoder to output the predicted labels in a top-down manner, where the predicted higher-level labels are directly used to guide the label prediction at the current level.
WR-ONE2SET: Towards Well-Calibrated Keyphrase Generation
Keyphrase generation aims to automatically generate short phrases summarizing an input document.
Boosting Verified Training for Robust Image Classifications via Abstraction
Via abstraction, all perturbed images are mapped into intervals before feeding into neural networks for training.
A Neural Divide-and-Conquer Reasoning Framework for Image Retrieval from Linguistically Complex Text
Inspired by the Divide-and-Conquer algorithm and dual-process theory, in this paper, we regard linguistically complex texts as compound proposition texts composed of multiple simple proposition sentences and propose an end-to-end Neural Divide-and-Conquer Reasoning framework, dubbed NDCR.
Scene Graph as Pivoting: Inference-time Image-free Unsupervised Multimodal Machine Translation with Visual Scene Hallucination
In this work, we investigate a more realistic unsupervised multimodal machine translation (UMMT) setup, inference-time image-free UMMT, where the model is trained with source-text image pairs, and tested with only source-text inputs.
Language Generation from Brain Recordings
Inspired by recent research that revealed associations between the brain and the large computational language models, we propose a generative language BCI that utilizes the capacity of a large language model (LLM) jointly with a semantic brain decoder to directly generate language from functional magnetic resonance imaging (fMRI) input.
Improving Relation Extraction with Relational Paraphrase Sentences
In this paper, we propose an alternative approach to improve RE systems via enriching diverse expressions by relational paraphrase sentences.
Bridging the Gap between Training and Inference: Multi-Candidate Optimization for Diverse Neural Machine Translation
Diverse NMT aims at generating multiple diverse yet faithful translations given a source sentence.
Third-Party Aligner for Neural Word Alignments
Word alignment is to find translationally equivalent words between source and target sentences.
How Well Do Large Language Models Understand Syntax? An Evaluation by Asking Natural Language Questions
While recent advancements in large language models (LLMs) bring us closer to achieving artificial general intelligence, the question persists: Do LLMs truly understand language, or do they merely mimic comprehension through pattern recognition?
GenView: Enhancing View Quality with Pretrained Generative Model for Self-Supervised Learning
To tackle these challenges, we present GenView, a controllable framework that augments the diversity of positive views leveraging the power of pretrained generative models while preserving semantics.
In-Context Learning State Vector with Inner and Momentum Optimization
In this paper, we address this gap by presenting a comprehensive analysis of these compressed vectors, drawing parallels to the parameters trained with gradient descent, and introduce the concept of state vector.
Token Drop mechanism for Neural Machine Translation
Neural machine translation with millions of parameters is vulnerable to unfamiliar inputs.
Semantic Role Labeling with Heterogeneous Syntactic Knowledge
Recently, due to the interplay between syntax and semantics, incorporating syntactic knowledge into neural semantic role labeling (SRL) has achieved much attention.
Adversarial Self-Attention for Language Understanding
Deep neural models (e. g. Transformer) naturally learn spurious features, which create a ``shortcut'' between the labels and inputs, thus impairing the generalization and robustness.
Ranked #1 on
Machine Reading Comprehension
on DREAM
Machine Reading Comprehension
Named Entity Recognition (NER)
+4
Towards Robust Neural Machine Translation with Iterative Scheduled Data-Switch Training
Most existing methods on robust neural machine translation (NMT) construct adversarial examples by injecting noise into authentic examples and indiscriminately exploit two types of examples.
AMOM: Adaptive Masking over Masking for Conditional Masked Language Model
Experiments on \textbf{3} different tasks (neural machine translation, summarization, and code generation) with \textbf{15} datasets in total confirm that our proposed simple method achieves significant performance improvement over the strong CMLM model.
ExplainCPE: A Free-text Explanation Benchmark of Chinese Pharmacist Examination
As ChatGPT and GPT-4 spearhead the development of Large Language Models (LLMs), more researchers are investigating their performance across various tasks.
A Read-and-Select Framework for Zero-shot Entity Linking
Zero-shot entity linking (EL) aims at aligning entity mentions to unseen entities to challenge the generalization ability.
Revisiting Sparse Retrieval for Few-shot Entity Linking
Entity linking aims to link ambiguous mentions to their corresponding entities in a knowledge base.
A Multimodal In-Context Tuning Approach for E-Commerce Product Description Generation
For this setting, previous methods utilize visual and textual encoders to encode the image and keywords and employ a language model-based decoder to generate the product description.
VisionGraph: Leveraging Large Multimodal Models for Graph Theory Problems in Visual Context
Large Multimodal Models (LMMs) have achieved impressive success in visual understanding and reasoning, remarkably improving the performance of mathematical reasoning in a visual context.
MuCPAD: A Multi-Domain Chinese Predicate-Argument Dataset
1) Based on a frame-free annotation methodology, we avoid writing complex frames for new predicates.
Chunk-aware Alignment and Lexical Constraint for Visual Entailment with Natural Language Explanations
CSI), a relation inferrer, and a Lexical Constraint-aware Generator (arr.
Holistic Exploration on Universal Decompositional Semantic Parsing: Architecture, Data Augmentation, and LLM Paradigm
In this paper, we conduct a holistic exploration of the Universal Decompositional Semantic (UDS) Parsing.
Relevance Feedback with Brain Signals
To explore the effectiveness of brain signals in the context of RF, we propose a novel RF framework that combines BCI-based relevance feedback with pseudo-relevance signals and implicit signals to improve the performance of document re-ranking.
OpenBA-V2: Reaching 77.3% High Compression Ratio with Fast Multi-Stage Pruning
Large Language Models (LLMs) have played an important role in many fields due to their powerful capabilities. However, their massive number of parameters leads to high deployment requirements and incurs significant inference costs, which impedes their practical applications.
Using active learning to expand training data for implicit discourse relation recognition
We tackle discourse-level relation recognition, a problem of determining semantic relations between text spans.
Loss re-scaling VQA: Revisiting the LanguagePrior Problem from a Class-imbalance View
Concretely, we design a novel interpretation scheme whereby the loss of mis-predicted frequent and sparse answers of the same question type is distinctly exhibited during the late training phase.
A Coarse-to-Fine Labeling Framework for Joint Word Segmentation, POS Tagging, and Constituent Parsing
In the coarse labeling stage, the joint model outputs a bracketed tree, in which each node corresponds to one of four labels (i. e., phrase, subphrase, word, subword).
Enhancing Image Rescaling using Dual Latent Variables in Invertible Neural Network
Normalizing flow models have been used successfully for generative image super-resolution (SR) by approximating complex distribution of natural images to simple tractable distribution in latent space through Invertible Neural Networks (INN).
SHLE: Devices Tracking and Depth Filtering for Stereo-based Height Limit Estimation
In stage 1, a novel devices detection and tracking scheme is introduced, which accurately locate the height limit devices in the left or right image.
A Pilot Study on Dialogue-Level Dependency Parsing for Chinese
Dialogue-level dependency parsing has received insufficient attention, especially for Chinese.
Unbiased Delayed Feedback Label Correction for Conversion Rate Prediction
To directly introduce the correct feedback label information, we propose an Unbiased delayed feedback Label Correction framework (ULC), which uses an auxiliary model to correct labels for observed negative feedback samples.
Separate the Wheat from the Chaff: Model Deficiency Unlearning via Parameter-Efficient Module Operation
To evaluate the effectiveness of our approach in terms of truthfulness and detoxification, we conduct extensive experiments on LLMs, encompassing additional abilities such as language modeling and mathematical reasoning.
G-SPEED: General SParse Efficient Editing MoDel
Large Language Models~(LLMs) have demonstrated incredible capabilities in understanding, generating, and manipulating languages.
Beyond Hard Samples: Robust and Effective Grammatical Error Correction with Cycle Self-Augmenting
By leveraging the augmenting data from the GEC models themselves in the post-training process and introducing regularization data for cycle training, our proposed method can effectively improve the model robustness of well-trained GEC models with only a few more training epochs as an extra cost.
Clustering Pseudo Language Family in Multilingual Translation Models with Fisher Information Matrix
In multilingual translation research, the comprehension and utilization of language families are of paramount importance.
To Recommend or Not: Recommendability Identification in Conversations with Pre-trained Language Models
First, we propose and define the recommendability identification task, which investigates the need for recommendations in the current conversational context.
Common Sense Enhanced Knowledge-based Recommendation with Large Language Model
Knowledge-based recommendation models effectively alleviate the data sparsity issue leveraging the side information in the knowledge graph, and have achieved considerable performance.
Adaptive Weighting for Neural Machine Translation
In the popular sequence to sequence (seq2seq) neural machine translation (NMT), there exist many weighted sum models (WSMs), each of which takes a set of input and generates one output.
Is POS Tagging Necessary or Even Helpful for Neural Dependency Parsing?
In the pre deep learning era, part-of-speech tags have been considered as indispensable ingredients for feature engineering in dependency parsing.
Towards a Better Understanding Human Reading Comprehension with Brain Signals
In this paper, we carefully design a lab-based user study to investigate brain activities during reading comprehension.
Language Anisotropic Cross-Lingual Model Editing
On the newly defined cross-lingual model editing task, we empirically demonstrate the failure of monolingual baselines in propagating the edit to multiple languages and the effectiveness of the proposed language anisotropic model editing.
Measuring Item Global Residual Value for Fair Recommendation
In this paper, we call for a shift of attention from modeling user preferences to developing fair exposure mechanisms for items.
MAP: Towards Balanced Generalization of IID and OOD through Model-Agnostic Adapters
To achieve this goal, we apply a bilevel optimization to explicitly model and optimize the coupling relationship between the OOD model and auxiliary adapter layers.
LLM-enhanced Self-training for Cross-domain Constituency Parsing
Our experimental results demonstrate that self-training for constituency parsing, equipped with an LLM, outperforms traditional methods regardless of the LLM's performance.
Context Consistency between Training and Testing in Simultaneous Machine Translation
Simultaneous Machine Translation (SiMT) aims to yield a real-time partial translation with a monotonically growing the source-side context.
A Situation-aware Enhancer for Personalized Recommendation
In this paper, we provide a new perspective that takes situations as the preconditions for users' interactions.
3AM: An Ambiguity-Aware Multi-Modal Machine Translation Dataset
Experimental results show that MMT models trained on our dataset exhibit a greater ability to exploit visual information than those trained on other MMT datasets.
Stacked AMR Parsing with Silver Data
In particular, one recent seq-to-seq work directly fine-tunes AMR graph sequences on the encoder-decoder pre-trained language model and achieves new state-of-the-art results, outperforming previous works by a large margin.
Identifying Chinese Opinion Expressions with Extremely-Noisy Crowdsourcing Annotations
Recent works of opinion expression identification (OEI) rely heavily on the quality and scale of the manually-constructed training corpus, which could be extremely difficult to satisfy.
Extending Phrase Grounding with Pronouns in Visual Dialogues
First, we construct a dataset of phrase grounding with both noun phrases and pronouns to image regions.
Semantic Structure Enhanced Contrastive Adversarial Hash Network for Cross-media Representation Learning
Secondly, for further improving learning ability of implicit cross-media semantic associations, a semantic label association graph is constructed, and the graph convolutional network is utilized to mine the implicit semantic structures, thus guiding learning of discriminative features of different modalities.
Revisiting Token Dropping Strategy in Efficient BERT Pretraining
Token dropping is a recently-proposed strategy to speed up the pretraining of masked language models, such as BERT, by skipping the computation of a subset of the input tokens at several middle layers.
Empower Nested Boolean Logic via Self-Supervised Curriculum Learning
Beyond the great cognitive powers showcased by language models, it is crucial to scrutinize whether their reasoning capabilities stem from strong generalization or merely exposure to relevant data.
Caseformer: Pre-training for Legal Case Retrieval Based on Inter-Case Distinctions
Legal case retrieval aims to help legal workers find relevant cases related to their cases at hand, which is important for the guarantee of fairness and justice in legal judgments.
HA-HI: Synergising fMRI and DTI through Hierarchical Alignments and Hierarchical Interactions for Mild Cognitive Impairment Diagnosis
Early diagnosis of mild cognitive impairment (MCI) and subjective cognitive decline (SCD) utilizing multi-modal magnetic resonance imaging (MRI) is a pivotal area of research.
FIPO: Free-form Instruction-oriented Prompt Optimization with Preference Dataset and Modular Fine-tuning Schema
In the quest to facilitate the deep intelligence of Large Language Models (LLMs) accessible in final-end user-bot interactions, the art of prompt crafting emerges as a critical yet complex task for the average user.
Adversarial Learning for Chinese NER from Crowd Annotations
To quickly obtain new labeled data, we can choose crowdsourcing as an alternative way at lower cost in a short time.
Chinese Named Entity Recognition
named-entity-recognition
+2
Improved English to Russian Translation by Neural Suffix Prediction
Neural machine translation (NMT) suffers a performance deficiency when a limited vocabulary fails to cover the source or target side adequately, which happens frequently when dealing with morphologically rich languages.
SEE: Syntax-aware Entity Embedding for Neural Relation Extraction
First, we encode the context of entities on a dependency tree as sentence-level entity embedding based on tree-GRU.
Translating Phrases in Neural Machine Translation
Otherwise, the NMT decoder generates a word from the vocabulary as the general NMT decoder does.
Modeling Source Syntax for Neural Machine Translation
Even though a linguistics-free sequence to sequence model in neural machine translation (NMT) has certain capability of implicitly learning syntactic information of source sentences, this paper shows that source syntax can be explicitly incorporated into NMT effectively to provide further improvements.
Neural Machine Translation Advised by Statistical Machine Translation
Neural Machine Translation (NMT) is a new approach to machine translation that has made great progress in recent years.
Word Segmentation on Micro-blog Texts with External Lexicon and Heterogeneous Data
This paper describes our system designed for the NLPCC 2016 shared task on word segmentation on micro-blog texts.
Training Dependency Parsers with Partial Annotation
The first approach is previously proposed to directly train a log-linear graph-based parser (LLGPar) with PA based on a forest-based objective.
Boost Phrase-level Polarity Labelling with Review-level Sentiment Classification
In this paper, we focus on the problem of phrase-level sentiment polarity labelling and attempt to bridge the gap between phrase-level and review-level sentiment analysis.
Minimum $n$-Rank Approximation via Iterative Hard Thresholding
Additionally, combining an effective heuristic for determining $n$-rank, we can also apply the proposed algorithm to solve MnRA when $n$-rank is unknown in advance.
Classification of lung nodules in CT images based on Wasserstein distance in differential geometry
The Wasserstein distance between the nodules is calculated based on our new spherical optimal mass transport, this new algorithm works directly on sphere by using spherical metric, which is much more accurate and efficient than previous methods.
LineNet: a Zoomable CNN for Crowdsourced High Definition Maps Modeling in Urban Environments
Combining LineNet and TTLane, we proposed a pipeline to model HD maps with crowdsourced data for the first time.
Sentiment Classification towards Question-Answering with Hierarchical Matching Network
On the basis, we propose a three-stage hierarchical matching network to explore deep sentiment information in a QA text pair.
Supervised Treebank Conversion: Data and Approaches
Treebank conversion is a straightforward and effective way to exploit various heterogeneous treebanks for boosting parsing performance.
Multi-Grained Chinese Word Segmentation
Traditionally, word segmentation (WS) adopts the single-grained formalism, where a sentence corresponds to a single word sequence.
Report of NEWS 2018 Named Entity Transliteration Shared Task
This report presents the results from the Named Entity Transliteration Shared Task conducted as part of The Seventh Named Entities Workshop (NEWS 2018) held at ACL 2018 in Melbourne, Australia.
One vs. Many QA Matching with both Word-level and Sentence-level Attention Network
Question-Answer (QA) matching is a fundamental task in the Natural Language Processing community.
Learning Event Expressions via Bilingual Structure Projection
Aiming to resolve high complexities of event descriptions, previous work (Huang and Riloff, 2013) proposes multi-faceted event recognition and a bootstrapping method to automatically acquire both event facet phrases and event expressions from unannotated texts.
Distributed Representations for Building Profiles of Users and Items from Text Reviews
In this paper, we propose an approach to learn distributed representations of users and items from text comments for recommendation systems.
