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Found 62 papers, 34 papers with code
Iterative Constrained Back-Translation for Unsupervised Domain Adaptation of Machine Translation
In this paper, we propose an Iterative Constrained Back-Translation (ICBT) method to incorporate in-domain lexical knowledge on the basis of BT for unsupervised domain adaptation of NMT.
Saliency-based Multi-View Mixed Language Training for Zero-shot Cross-lingual Classification
Recent multilingual pre-trained models, like XLM-RoBERTa (XLM-R), have been demonstrated effective in many cross-lingual tasks.
Cross-Lingual Sentiment Classification
Dialogue State Tracking
+4
A Joint Model for Graph-based Chinese Dependency Parsing
The experimental results on the Penn Chinese treebank (CTB5) show that our proposed joint model improved by 0. 38% on dependency parsing than the model of Yan et al. (2019).
基于多任务标签一致性机制的中文命名实体识别(Chinese Named Entity Recognition based on Multi-task Label Consistency Mechanism)
“实体边界预测对中文命名实体识别至关重要。现有研究为改善边界识别效果提出的多任务学习方法仅考虑与分词任务结合, 缺少多任务标签训练数据, 无法学到任务的标签一致性关系。本文提出一种新的基于多任务标签一致性机制的中文命名实体识别方法:将分词和词性信息融入命名实体识别模型, 使三种任务联合训练;建立基于标签一致性机制的多任务学习模式, 来捕获标签一致性关系及学习多任务表示。全样本和小样本实验表明了方法的有效性。”
Chinese Named Entity Recognition
named-entity-recognition
+1
Saliency as Evidence: Event Detection with Trigger Saliency Attribution
Event detection (ED) is a critical subtask of event extraction that seeks to identify event triggers of certain types in texts. Despite significant advances in ED, existing methods typically follow a “one model fits all types” approach, which sees no differences between event types and often results in a quite skewed performance. Finding the causes of skewed performance is crucial for the robustness of an ED model, but to date there has been little exploration of this problem. This research examines the issue in depth and presents a new concept termed trigger salience attribution, which can explicitly quantify the underlying patterns of events.
融合外部知识的开放域复述模板获取方法(An Open Domain Paraphrasing Template Acquisition Method Based on External Knowledge)
“如何挖掘语言资源中丰富的复述模板, 是复述研究中的一项重要任务。已有方法在人工给定种子实体对的基础上, 利用实体关系, 通过自举迭代方式, 从开放域获取复述模板, 规避对平行语料或可比语料的依赖, 但是该方法需人工给定实体对, 实体关系受限;在迭代过程中语义会发生偏移, 影响获取质量。针对这些问题, 我们考虑知识库中包含描述特定语义关系的实体对(即关系三元组), 提出融合外部知识的开放域复述模板自动获取方法。首先, 将关系三元组与开放域文本对齐, 获取关系对应文本, 并将文本中语义丰富部分泛化成变量槽, 获取关系模板;接着设计模板表示方法, 本文利用预训练语言模型, 在模板表示中融合变量槽语义;最后, 根据获得的模板表示, 设计自动聚类与筛选方法, 获取高精度的复述模板。在融合自动评测与人工评测的评价方法下, 实验结果表明, 本文提出的方法实现了在开放域数据上复述模板的自动泛化与获取, 能够获得质量高、语义一致的复述模板。”
Syntactically-Informed Unsupervised Paraphrasing with Non-Parallel Data
Particularly, we design a two-stage learning method to effectively train the model using non-parallel data.
Learning Structural Information for Syntax-Controlled Paraphrase Generation
To model the alignment relation between words and nodes, we propose an attention regularization objective, which makes the decoder accurately select corresponding syntax nodes to guide the generation of words. Experiments show that SI-SCP achieves state-of-the-art performances in terms of semantic and syntactic quality on two popular benchmark datasets. Additionally, we propose a Syntactic Template Retriever (STR) to retrieve compatible syntactic structures.
Machine Reading Comprehension as Data Augmentation: A Case Study on Implicit Event Argument Extraction
Implicit event argument extraction (EAE) is a crucial document-level information extraction task that aims to identify event arguments beyond the sentence level.
Apollo-Forecast: Overcoming Aliasing and Inference Speed Challenges in Language Models for Time Series Forecasting
Encoding time series into tokens and using language models for processing has been shown to substantially augment the models' ability to generalize to unseen tasks.
Textualized Agent-Style Reasoning for Complex Tasks by Multiple Round LLM Generation
Chain-of-thought prompting significantly boosts the reasoning ability of large language models but still faces three issues: hallucination problem, restricted interpretability, and uncontrollable generation.
Beyond Binary Gender: Evaluating Gender-Inclusive Machine Translation with Ambiguous Attitude Words
This study presents a benchmark AmbGIMT (Gender-Inclusive Machine Translation with Ambiguous attitude words), which assesses gender bias beyond binary gender.
Dual-Space Knowledge Distillation for Large Language Models
Furthermore, this discrepancy also hinders the KD process between models with different vocabularies, which is common for current LLMs.
Multilingual Knowledge Editing with Language-Agnostic Factual Neurons
To address this issue, we first investigate how LLMs process multilingual factual knowledge and discover that the same factual knowledge in different languages generally activates a shared set of neurons, which we call language-agnostic factual neurons (LAFNs).
Outdated Issue Aware Decoding for Reasoning Questions on Edited Knowledge
Consequently, on the reasoning questions, we discover that existing methods struggle to utilize the edited knowledge to reason the new answer, and tend to retain outdated responses, which are generated by the original models utilizing original knowledge.
LCS: A Language Converter Strategy for Zero-Shot Neural Machine Translation
Multilingual neural machine translation models generally distinguish translation directions by the language tag (LT) in front of the source or target sentences.
Comments as Natural Logic Pivots: Improve Code Generation via Comment Perspective
In this paper, we suggest that code comments are the natural logic pivot between natural language and code language and propose using comments to boost the code generation ability of code LLMs.
TransportationGames: Benchmarking Transportation Knowledge of (Multimodal) Large Language Models
Large language models (LLMs) and multimodal large language models (MLLMs) have shown excellent general capabilities, even exhibiting adaptability in many professional domains such as law, economics, transportation, and medicine.
Towards Faster k-Nearest-Neighbor Machine Translation
The results show that our method succeeds in reducing redundant retrieval operations and significantly reduces the overhead of kNN retrievals by up to 53% at the expense of a slight decline in translation quality.
Enhancing Machine Translation through Advanced In-Context Learning: A Methodological Strategy for GPT-4 Improvement
It represents a significant step forward in machine learning, leveraging the inherent capabilities of GPT-4 to provide translations that are not only accurate but also contextually rich and linguistically sophisticated.
Get the Ball Rolling: Alerting Autonomous Robots When to Help to Close the Healthcare Loop
To facilitate the advancement of research in healthcare robots without human intervention or commands, we introduce the Autonomous Helping Challenge, along with a crowd-sourcing large-scale dataset.
A Quality-based Syntactic Template Retriever for Syntactically-controlled Paraphrase Generation
Furthermore, for situations requiring multiple paraphrases for each source sentence, we design a Diverse Templates Search (DTS) algorithm, which can enhance the diversity between paraphrases without sacrificing quality.
Improving Translation Faithfulness of Large Language Models via Augmenting Instructions
The experimental results demonstrate significant improvements in translation performance with SWIE based on BLOOMZ-3b, particularly in zero-shot and long text translations due to reduced instruction forgetting risk.
CollabKG: A Learnable Human-Machine-Cooperative Information Extraction Toolkit for (Event) Knowledge Graph Construction
In order to construct or extend entity-centric and event-centric knowledge graphs (KG and EKG), the information extraction (IE) annotation toolkit is essential.
D$^2$TV: Dual Knowledge Distillation and Target-oriented Vision Modeling for Many-to-Many Multimodal Summarization
Further, we propose a dual knowledge distillation and target-oriented vision modeling framework for the M$^3$S task.
Towards Understanding and Improving Knowledge Distillation for Neural Machine Translation
In this work, we first unravel this mystery from an empirical perspective and show that the knowledge comes from the top-1 predictions of teachers, which also helps us build a potential connection between word- and sequence-level KD.
Unified Model Learning for Various Neural Machine Translation
Specifically, we propose a ``versatile'' model, i. e., the Unified Model Learning for NMT (UMLNMT) that works with data from different tasks, and can translate well in multiple settings simultaneously, and theoretically it can be as many as possible.
Time Reversal Enabled Fiber-Optic Time Synchronization
It measures the clock difference between two locations without involving a data layer, which can reduce the complexity of the system.
ChatIE: Zero-Shot Information Extraction via Chatting with ChatGPT
Zero-shot information extraction (IE) aims to build IE systems from the unannotated text.
Summary-Oriented Vision Modeling for Multimodal Abstractive Summarization
However, less attention has been paid to the visual features from the perspective of the summary, which may limit the model performance, especially in the low- and zero-resource scenarios.
BJTU-WeChat's Systems for the WMT22 Chat Translation Task
Our systems achieve 0. 810 and 0. 946 COMET scores.
Improved Data Augmentation for Translation Suggestion
This paper introduces the system used in our submission to the WMT'22 Translation Suggestion shared task.
Cross-Align: Modeling Deep Cross-lingual Interactions for Word Alignment
Word alignment which aims to extract lexicon translation equivalents between source and target sentences, serves as a fundamental tool for natural language processing.
Robust, High-Rate Trajectory Tracking on Insect-Scale Soft-Actuated Aerial Robots with Deep-Learned Tube MPC
Accurate and agile trajectory tracking in sub-gram Micro Aerial Vehicles (MAVs) is challenging, as the small scale of the robot induces large model uncertainties, demanding robust feedback controllers, while the fast dynamics and computational constraints prevent the deployment of computationally expensive strategies.
Scheduled Multi-task Learning for Neural Chat Translation
Neural Chat Translation (NCT) aims to translate conversational text into different languages.
Generating Authentic Adversarial Examples beyond Meaning-preserving with Doubly Round-trip Translation
Generating adversarial examples for Neural Machine Translation (NMT) with single Round-Trip Translation (RTT) has achieved promising results by releasing the meaning-preserving restriction.
A Variational Hierarchical Model for Neural Cross-Lingual Summarization
The goal of the cross-lingual summarization (CLS) is to convert a document in one language (e. g., English) to a summary in another one (e. g., Chinese).
Conditional Bilingual Mutual Information Based Adaptive Training for Neural Machine Translation
Token-level adaptive training approaches can alleviate the token imbalance problem and thus improve neural machine translation, through re-weighting the losses of different target tokens based on specific statistical metrics (e. g., token frequency or mutual information).
MSCTD: A Multimodal Sentiment Chat Translation Dataset
In this work, we introduce a new task named Multimodal Chat Translation (MCT), aiming to generate more accurate translations with the help of the associated dialogue history and visual context.
Towards Making the Most of Dialogue Characteristics for Neural Chat Translation
Neural Chat Translation (NCT) aims to translate conversational text between speakers of different languages.
Scheduled Sampling Based on Decoding Steps for Neural Machine Translation
Its core motivation is to simulate the inference scene during training by replacing ground-truth tokens with predicted tokens, thus bridging the gap between training and inference.
Modeling Bilingual Conversational Characteristics for Neural Chat Translation
Despite the impressive performance of sentence-level and context-aware Neural Machine Translation (NMT), there still remain challenges to translate bilingual conversational text due to its inherent characteristics such as role preference, dialogue coherence, and translation consistency.
Confidence-Aware Scheduled Sampling for Neural Machine Translation
In this way, the model is exactly exposed to predicted tokens for high-confidence positions and still ground-truth tokens for low-confidence positions.
Target-Oriented Fine-tuning for Zero-Resource Named Entity Recognition
In this paper, we tackle the problem by transferring knowledge from three aspects, i. e., domain, language and task, and strengthening connections among them.
Emotional Conversation Generation with Heterogeneous Graph Neural Network
Firstly, we design a Heterogeneous Graph-Based Encoder to represent the conversation content (i. e., the dialogue history, its emotion flow, facial expressions, audio, and speakers' personalities) with a heterogeneous graph neural network, and then predict suitable emotions for feedback.
Multi-view Classification Model for Knowledge Graph Completion
This paper describes a novel multi-view classification model for knowledge graph completion, where multiple classification views are performed based on both content and context information for candidate triple evaluation.
A Learning-Exploring Method to Generate Diverse Paraphrases with Multi-Objective Deep Reinforcement Learning
We propose a learning-exploring method to generate sentences as learning objectives from the learned data distribution, and employ reinforcement learning to combine these new learning objectives for model training.
Modeling Inter-Aspect Dependencies with a Non-temporal Mechanism for Aspect-Based Sentiment Analysis
For multiple aspects scenario of aspect-based sentiment analysis (ABSA), existing approaches typically ignore inter-aspect relations or rely on temporal dependencies to process aspect-aware representations of all aspects in a sentence.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+1
Faster Depth-Adaptive Transformers
Depth-adaptive neural networks can dynamically adjust depths according to the hardness of input words, and thus improve efficiency.
An Iterative Multi-Knowledge Transfer Network for Aspect-Based Sentiment Analysis
Aspect-based sentiment analysis (ABSA) mainly involves three subtasks: aspect term extraction, opinion term extraction, and aspect-level sentiment classification, which are typically handled in a separate or joint manner.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+3
A Dependency Syntactic Knowledge Augmented Interactive Architecture for End-to-End Aspect-based Sentiment Analysis
The aspect-based sentiment analysis (ABSA) task remains to be a long-standing challenge, which aims to extract the aspect term and then identify its sentiment orientation. In previous approaches, the explicit syntactic structure of a sentence, which reflects the syntax properties of natural language and hence is intuitively crucial for aspect term extraction and sentiment recognition, is typically neglected or insufficiently modeled.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+3
Depth-Adaptive Graph Recurrent Network for Text Classification
The Sentence-State LSTM (S-LSTM) is a powerful and high efficient graph recurrent network, which views words as nodes and performs layer-wise recurrent steps between them simultaneously.
Original Semantics-Oriented Attention and Deep Fusion Network for Sentence Matching
Unlike existing models, each attention layer of OSOA-DFN is oriented to the original semantic representation of another sentence, which captures the relevant information from a fixed matching target.
CM-Net: A Novel Collaborative Memory Network for Spoken Language Understanding
Spoken Language Understanding (SLU) mainly involves two tasks, intent detection and slot filling, which are generally modeled jointly in existing works.
Ranked #1 on
Slot Filling
on CAIS
A Novel Aspect-Guided Deep Transition Model for Aspect Based Sentiment Analysis
Aspect based sentiment analysis (ABSA) aims to identify the sentiment polarity towards the given aspect in a sentence, while previous models typically exploit an aspect-independent (weakly associative) encoder for sentence representation generation.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+1
GCDT: A Global Context Enhanced Deep Transition Architecture for Sequence Labeling
Current state-of-the-art systems for sequence labeling are typically based on the family of Recurrent Neural Networks (RNNs).
Ranked #20 on
Named Entity Recognition (NER)
on CoNLL 2003 (English)
(using extra training data)
System Description of bjtu\_nlp Neural Machine Translation System
This paper presents our machine translation system that developed for the WAT2016 evalua-tion tasks of ja-en, ja-zh, en-ja, zh-ja, JPCja-en, JPCja-zh, JPCen-ja, JPCzh-ja.
