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Found 68 papers, 18 papers with code
Syntax in End-to-End Natural Language Processing
This tutorial surveys the latest technical progress of syntactic parsing and the role of syntax in end-to-end natural language processing (NLP) tasks, in which semantic role labeling (SRL) and machine translation (MT) are the representative NLP tasks that have always been beneficial from informative syntactic clues since a long time ago, though the advance from end-to-end deep learning models shows new results.
Synchronous Refinement for Neural Machine Translation
Machine translation typically adopts an encoder-to-decoder framework, in which the decoder generates the target sentence word-by-word in an auto-regressive manner.
SJTU-NICT’s Supervised and Unsupervised Neural Machine Translation Systems for the WMT20 News Translation Task
In this paper, we introduced our joint team SJTU-NICT ‘s participation in the WMT 2020 machine translation shared task.
Lost in Benchmarks? Rethinking Large Language Model Benchmarking with Item Response Theory
The evaluation of large language models (LLMs) via benchmarks is widespread, yet inconsistencies between different leaderboards and poor separability among top models raise concerns about their ability to accurately reflect authentic model capabilities.
MoK-RAG: Mixture of Knowledge Paths Enhanced Retrieval-Augmented Generation for Embodied AI Environments
While human cognition inherently retrieves information from diverse and specialized knowledge sources during decision-making processes, current Retrieval-Augmented Generation (RAG) systems typically operate through single-source knowledge retrieval, leading to a cognitive-algorithmic discrepancy.
Representation-based Reward Modeling for Efficient Safety Alignment of Large Language Model
Reinforcement Learning (RL) algorithms for safety alignment of Large Language Models (LLMs), such as Direct Preference Optimization (DPO), encounter the challenge of distribution shift.
Adaptive Inner Speech-Text Alignment for LLM-based Speech Translation
Recent advancement of large language models (LLMs) has led to significant breakthroughs across various tasks, laying the foundation for the development of LLM-based speech translation systems.
XIFBench: Evaluating Large Language Models on Multilingual Instruction Following
Large Language Models (LLMs) have demonstrated remarkable instruction-following capabilities across various applications.
Merge then Realign: Simple and Effective Modality-Incremental Continual Learning for Multimodal LLMs
Considering the heavy cost of training MLLMs, it is necessary to reuse the existing ones and further extend them to more modalities through Modality-incremental Continual Learning (MCL).
Generator-Assistant Stepwise Rollback Framework for Large Language Model Agent
Large language model (LLM) agents typically adopt a step-by-step reasoning framework, in which they interleave the processes of thinking and acting to accomplish the given task.
The Rise of Darkness: Safety-Utility Trade-Offs in Role-Playing Dialogue Agents
Large Language Models (LLMs) have made remarkable advances in role-playing dialogue agents, demonstrating their utility in character simulations.
ASurvey: Spatiotemporal Consistency in Video Generation
Video generation, by leveraging a dynamic visual generation method, pushes the boundaries of Artificial Intelligence Generated Content (AIGC).
Evaluating o1-Like LLMs: Unlocking Reasoning for Translation through Comprehensive Analysis
The o1-Like LLMs are transforming AI by simulating human cognitive processes, but their performance in multilingual machine translation (MMT) remains underexplored.
Exploring Translation Mechanism of Large Language Models
Large language models (LLMs) have succeeded remarkably in multilingual translation tasks.
Benchmarking and Improving Large Vision-Language Models for Fundamental Visual Graph Understanding and Reasoning
To study the reason behind these limitations, we propose VGCure, a comprehensive benchmark covering 22 tasks for examining the fundamental graph understanding and reasoning capacities of LVLMs.
LinguaLIFT: An Effective Two-stage Instruction Tuning Framework for Low-Resource Language Tasks
Large language models (LLMs) have demonstrated impressive multilingual understanding and reasoning capabilities, driven by extensive pre-training multilingual corpora and fine-tuning instruction data.
Make Imagination Clearer! Stable Diffusion-based Visual Imagination for Multimodal Machine Translation
Visual information has been introduced for enhancing machine translation (MT), and its effectiveness heavily relies on the availability of large amounts of bilingual parallel sentence pairs with manual image annotations.
LLM-based Discriminative Reasoning for Knowledge Graph Question Answering
Large language models (LLMs) based on generative pre-trained Transformer have achieved remarkable performance on knowledge graph question-answering (KGQA) tasks.
ZigZagkv: Dynamic KV Cache Compression for Long-context Modeling based on Layer Uncertainty
To accelerate the inference of LLMs, storing computed caches in memory has become the standard technique.
Look Before You Leap: Enhancing Attention and Vigilance Regarding Harmful Content with GuidelineLLM
This vulnerability poses significant risks to the real-world applications.
MoDification: Mixture of Depths Made Easy
Long-context efficiency has recently become a trending topic in serving large language models (LLMs).
LLM-based Translation Inference with Iterative Bilingual Understanding
The remarkable understanding and generation capabilities of large language models (LLMs) have greatly improved translation performance.
Question-guided Knowledge Graph Re-scoring and Injection for Knowledge Graph Question Answering
Knowledge graph question answering (KGQA) involves answering natural language questions by leveraging structured information stored in a knowledge graph.
Dynamic Planning for LLM-based Graphical User Interface Automation
However, a key challenge lies in devising effective plans to guide action prediction in GUI tasks, though planning have been widely recognized as effective for decomposing complex tasks into a series of steps.
MemLong: Memory-Augmented Retrieval for Long Text Modeling
This work introduces MemLong: Memory-Augmented Retrieval for Long Text Generation, a method designed to enhance the capabilities of long-context language modeling by utilizing an external retriever for historical information retrieval.
TF-Attack: Transferable and Fast Adversarial Attacks on Large Language Models
In this paper, we analyze the core mechanisms of previous predominant adversarial attack methods, revealing that 1) the distributions of importance score differ markedly among victim models, restricting the transferability; 2) the sequential attack processes induces substantial time overheads.
LLM with Relation Classifier for Document-Level Relation Extraction
Large language models (LLMs) have created a new paradigm for natural language processing.
Large Language Models for Classical Chinese Poetry Translation: Benchmarking, Evaluating, and Improving
Different from the traditional translation tasks, classical Chinese poetry translation requires both adequacy and fluency in translating culturally and historically significant content and linguistic poetic elegance.
Understanding the RoPE Extensions of Long-Context LLMs: An Attention Perspective
Enabling LLMs to handle lengthy context is currently a research hotspot.
A Survey on Human Preference Learning for Large Language Models
In this survey, we review the progress in exploring human preference learning for LLMs from a preference-centered perspective, covering the sources and formats of preference feedback, the modeling and usage of preference signals, as well as the evaluation of the aligned LLMs.
On the Hallucination in Simultaneous Machine Translation
It is widely known that hallucination is a critical issue in Simultaneous Machine Translation (SiMT) due to the absence of source-side information.
DUAL-REFLECT: Enhancing Large Language Models for Reflective Translation through Dual Learning Feedback Mechanisms
Recently, large language models (LLMs) enhanced by self-reflection have achieved promising performance on machine translation.
Paying More Attention to Source Context: Mitigating Unfaithful Translations from Large Language Model
Large language models (LLMs) have showcased impressive multilingual machine translation ability.
Feature-based Low-Rank Compression of Large Language Models via Bayesian Optimization
To address the challenges of low-rank compression in LLMs, we conduct empirical research on the low-rank characteristics of large models.
Unsupervised Sign Language Translation and Generation
Motivated by the success of unsupervised neural machine translation (UNMT), we introduce an unsupervised sign language translation and generation network (USLNet), which learns from abundant single-modality (text and video) data without parallel sign language data.
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.
Universal Multimodal Representation for Language Understanding
Representation learning is the foundation of natural language processing (NLP).
Document-Level Relation Extraction with Sentences Importance Estimation and Focusing
Document-level relation extraction (DocRE) aims to determine the relation between two entities from a document of multiple sentences.
Ranked #5 on
Dialog Relation Extraction
on DialogRE
(F1c (v1) metric)
Dialog Relation Extraction
Document-level Relation Extraction
+2
Discriminative Reasoning for Document-level Relation Extraction
Document-level relation extraction (DocRE) models generally use graph networks to implicitly model the reasoning skill (i. e., pattern recognition, logical reasoning, coreference reasoning, etc.)
Ranked #24 on
Relation Extraction
on DocRED
Text Compression-aided Transformer Encoding
In this paper, we propose explicit and implicit text compression approaches to enhance the Transformer encoding and evaluate models using this approach on several typical downstream tasks that rely on the encoding heavily.
Prior Knowledge Representation for Self-Attention Networks
Self-attention networks (SANs) have shown promising empirical results in various natural language processing tasks.
Document-Level Relation Extraction with Reconstruction
In document-level relation extraction (DocRE), graph structure is generally used to encode relation information in the input document to classify the relation category between each entity pair, and has greatly advanced the DocRE task over the past several years.
Ranked #35 on
Relation Extraction
on DocRED
Robust Machine Reading Comprehension by Learning Soft labels
Neural models have achieved great success on the task of machine reading comprehension (MRC), which are typically trained on hard labels.
SJTU-NICT's Supervised and Unsupervised Neural Machine Translation Systems for the WMT20 News Translation Task
In this paper, we introduced our joint team SJTU-NICT 's participation in the WMT 2020 machine translation shared task.
Content Word Aware Neural Machine Translation
Neural machine translation (NMT) encodes the source sentence in a universal way to generate the target sentence word-by-word.
Neural Machine Translation with Universal Visual Representation
Though visual information has been introduced for enhancing neural machine translation (NMT), its effectiveness strongly relies on the availability of large amounts of bilingual parallel sentence pairs with manual image annotations.
Data-dependent Gaussian Prior Objective for Language Generation
However, MLE focuses on once-to-all matching between the predicted sequence and gold-standard, consequently treating all incorrect predictions as being equally incorrect.
Knowledge Distillation for Multilingual Unsupervised Neural Machine Translation
Unsupervised neural machine translation (UNMT) has recently achieved remarkable results for several language pairs.
Self-Training for Unsupervised Neural Machine Translation in Unbalanced Training Data Scenarios
Unsupervised neural machine translation (UNMT) that relies solely on massive monolingual corpora has achieved remarkable results in several translation tasks.
Explicit Reordering for Neural Machine Translation
Thus, we propose a novel reordering method to explicitly model this reordering information for the Transformer-based NMT.
Robust Unsupervised Neural Machine Translation with Adversarial Denoising Training
Unsupervised neural machine translation (UNMT) has recently attracted great interest in the machine translation community.
Modeling Future Cost for Neural Machine Translation
Existing neural machine translation (NMT) systems utilize sequence-to-sequence neural networks to generate target translation word by word, and then make the generated word at each time-step and the counterpart in the references as consistent as possible.
Explicit Sentence Compression for Neural Machine Translation
In this paper, we propose an explicit sentence compression method to enhance the source sentence representation for NMT.
Probing Contextualized Sentence Representations with Visual Awareness
We present a universal framework to model contextualized sentence representations with visual awareness that is motivated to overcome the shortcomings of the multimodal parallel data with manual annotations.
Recurrent Positional Embedding for Neural Machine Translation
To address this issue, this work proposes a recurrent positional embedding approach based on word vector.
English-Myanmar Supervised and Unsupervised NMT: NICT's Machine Translation Systems at WAT-2019
This paper presents the NICT{'}s participation (team ID: NICT) in the 6th Workshop on Asian Translation (WAT-2019) shared translation task, specifically Myanmar (Burmese) - English task in both translation directions.
Document-level Neural Machine Translation with Associated Memory Network
Most existing document-level NMT approaches are satisfied with a smattering sense of global document-level information, while this work focuses on exploiting detailed document-level context in terms of a memory network.
Revisiting Simple Domain Adaptation Methods in Unsupervised Neural Machine Translation
However, it has not been well-studied for unsupervised neural machine translation (UNMT), although UNMT has recently achieved remarkable results in several domain-specific language pairs.
NICT's Supervised Neural Machine Translation Systems for the WMT19 News Translation Task
In this paper, we describe our supervised neural machine translation (NMT) systems that we developed for the news translation task for Kazakh↔English, Gujarati↔English, Chinese↔English, and English→Finnish translation directions.
NICT's Unsupervised Neural and Statistical Machine Translation Systems for the WMT19 News Translation Task
This paper presents the NICT{'}s participation in the WMT19 unsupervised news translation task.
Sentence-Level Agreement for Neural Machine Translation
The training objective of neural machine translation (NMT) is to minimize the loss between the words in the translated sentences and those in the references.
Unsupervised Bilingual Word Embedding Agreement for Unsupervised Neural Machine Translation
In previous methods, UBWE is first trained using non-parallel monolingual corpora and then this pre-trained UBWE is used to initialize the word embedding in the encoder and decoder of UNMT.
Neural Machine Translation with Reordering Embeddings
The reordering model plays an important role in phrase-based statistical machine translation.
Lattice-Based Transformer Encoder for Neural Machine Translation
To integrate different segmentations with the state-of-the-art NMT model, Transformer, we propose lattice-based encoders to explore effective word or subword representation in an automatic way during training.
Syntax-Directed Attention for Neural Machine Translation
In this paper, we extend local attention with syntax-distance constraint, to focus on syntactically related source words with the predicted target word, thus learning a more effective context vector for word prediction.
Context-Aware Smoothing for Neural Machine Translation
In Neural Machine Translation (NMT), each word is represented as a low-dimension, real-value vector for encoding its syntax and semantic information.
Instance Weighting for Neural Machine Translation Domain Adaptation
Instance weighting has been widely applied to phrase-based machine translation domain adaptation.
Neural Machine Translation with Source Dependency Representation
Source dependency information has been successfully introduced into statistical machine translation.
