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Found 91 papers, 62 papers with code
NJU’s submission to the WMT20 QE Shared Task
This paper describes our system of the sentence-level and word-level Quality Estimation Shared Task of WMT20.
Data Augmentation for Low-resource Word Segmentation and POS Tagging of Ancient Chinese Texts
Although SikuRoberta significantly boosts performance on WSG and POS tasks on ancient Chinese texts, the lack of labeled data still limits the performance of the model.
HW-TSC’s Participation at WMT 2021 Quality Estimation Shared Task
This paper presents our work in WMT 2021 Quality Estimation (QE) Shared Task.
Learning from Adjective-Noun Pairs: A Knowledge-enhanced Framework for Target-Oriented Multimodal Sentiment Classification
Target-oriented multimodal sentiment classification (TMSC) is a new subtask of aspect-based sentiment analysis, which aims to determine the sentiment polarity of the opinion target mentioned in a (sentence, image) pair.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+2
Meta-LMTC: Meta-Learning for Large-Scale Multi-Label Text Classification
However, the simple extension of meta-learning approaches to multi-label classification is sub-optimal for LMTC tasks due to long-tailed label distribution and coexisting of few- and zero-shot scenarios.
BiTIIMT: A Bilingual Text-infilling Method for Interactive Machine Translation
In this work, we propose a novel BiTIIMT system, Bilingual Text-Infilling for Interactive Neural Machine Translation.
GLAT: Glancing at Latent Variables for Parallel Text Generation
Recently, parallel text generation has received widespread attention due to its success in generation efficiency.
Towards Multi-label Unknown Intent Detection
In this paper, we propose a more desirable task, multi-label unknown intent detection, to detect whether the utterance contains the unknown intent, in which each utterance may contain multiple intents.
PATS: Process-Level Adaptive Thinking Mode Switching
Current large-language models (LLMs) typically adopt a fixed reasoning strategy, either simple or complex, for all questions, regardless of their difficulty.
Internal Bias in Reasoning Models leads to Overthinking
Through further interpretability experiments, we find that this behavior is largely driven by the model's excessive attention to the input section, which amplifies the influence of internal bias on its decision-making process.
Why Not Act on What You Know? Unleashing Safety Potential of LLMs via Self-Aware Guard Enhancement
Large Language Models (LLMs) have shown impressive capabilities across various tasks but remain vulnerable to meticulously crafted jailbreak attacks.
Trans-Zero: Self-Play Incentivizes Large Language Models for Multilingual Translation Without Parallel Data
The rise of Large Language Models (LLMs) has reshaped machine translation (MT), but multilingual MT still relies heavily on parallel data for supervised fine-tuning (SFT), facing challenges like data scarcity for low-resource languages and catastrophic forgetting.
Could Thinking Multilingually Empower LLM Reasoning?
In this paper, we explore the upper bound of harnessing multilingualism in reasoning tasks, suggesting that multilingual reasoning promises significantly (by nearly 10 Acc@$k$ points) and robustly (tolerance for variations in translation quality and language choice) higher upper bounds than English-only reasoning.
Elucidating the Design Space of Multimodal Protein Language Models
Multimodal protein language models (PLMs) integrate sequence and token-based structural information, serving as a powerful foundation for protein modeling, generation, and design.
Understanding LLMs' Cross-Lingual Context Retrieval: How Good It Is And Where It Comes From
The ability of cross-lingual context retrieval is a fundamental aspect of cross-lingual alignment of large language models (LLMs), where the model extracts context information in one language based on requests in another language.
Investigating and Scaling up Code-Switching for Multilingual Language Model Pre-Training
Large language models (LLMs) exhibit remarkable multilingual capabilities despite the extreme language imbalance in the pre-training data.
R-PRM: Reasoning-Driven Process Reward Modeling
Large language models (LLMs) inevitably make mistakes when performing step-by-step mathematical reasoning.
Process-based Self-Rewarding Language Models
Human-annotated preference data is used for training to further improve LLMs' performance, which is constrained by the upper limit of human performance.
Alleviating Distribution Shift in Synthetic Data for Machine Translation Quality Estimation
Quality Estimation (QE) models evaluate the quality of machine translations without reference translations, serving as the reward models for the translation task.
Generalizing From Short to Long: Effective Data Synthesis for Long-Context Instruction Tuning
The focus of research into modelling long context has been on how to model position and there has been little investigation into other important aspects of language modelling such as instruction tuning.
BenchMAX: A Comprehensive Multilingual Evaluation Suite for Large Language Models
Previous multilingual benchmarks focus primarily on simple understanding tasks, but for large language models(LLMs), we emphasize proficiency in instruction following, reasoning, long context understanding, code generation, and so on.
Extend Adversarial Policy Against Neural Machine Translation via Unknown Token
Generating adversarial examples contributes to mainstream neural machine translation~(NMT) robustness.
SLAM: Towards Efficient Multilingual Reasoning via Selective Language Alignment
Despite the significant improvements achieved by large language models (LLMs) in English reasoning tasks, these models continue to struggle with multilingual reasoning.
"I've Heard of You!": Generate Spoken Named Entity Recognition Data for Unseen Entities
Spoken named entity recognition (NER) aims to identify named entities from speech, playing an important role in speech processing.
Self-Evolution Knowledge Distillation for LLM-based Machine Translation
Knowledge distillation (KD) has shown great promise in transferring knowledge from larger teacher models to smaller student models.
DPLM-2: A Multimodal Diffusion Protein Language Model
In this paper, we introduce DPLM-2, a multimodal protein foundation model that extends discrete diffusion protein language model (DPLM) to accommodate both sequences and structures.
Formality is Favored: Unraveling the Learning Preferences of Large Language Models on Data with Conflicting Knowledge
We find that pretrained LLMs establish learning preferences similar to humans, i. e., preferences towards formal texts and texts with fewer spelling errors, resulting in faster learning and more favorable treatment of knowledge in data with such features when facing conflicts.
MoE-LPR: Multilingual Extension of Large Language Models through Mixture-of-Experts with Language Priors Routing
Then, the model reviews the knowledge of the original languages with replay data amounting to less than 1% of post-pretraining, where we incorporate language priors routing to better recover the abilities of the original languages.
EfficientRAG: Efficient Retriever for Multi-Hop Question Answering
Retrieval-augmented generation (RAG) methods encounter difficulties when addressing complex questions like multi-hop queries.
Hallu-PI: Evaluating Hallucination in Multi-modal Large Language Models within Perturbed Inputs
Extensive experiments on 12 mainstream MLLMs, such as GPT-4V and Gemini-Pro Vision, demonstrate that these models exhibit significant hallucinations on Hallu-PI, which is not observed in unperturbed scenarios.
PreAlign: Boosting Cross-Lingual Transfer by Early Establishment of Multilingual Alignment
In this paper, we propose PreAlign, a framework that establishes multilingual alignment prior to language model pretraining.
Multilingual Contrastive Decoding via Language-Agnostic Layers Skipping
Decoding by contrasting layers (DoLa), is designed to improve the generation quality of large language models (LLMs) by contrasting the prediction probabilities between an early exit output (amateur logits) and the final output (expert logits).
Large Language Models Are Cross-Lingual Knowledge-Free Reasoners
However, the relationship between capabilities in different languages is less explored.
Large Language Models are Limited in Out-of-Context Knowledge Reasoning
Using this dataset, we evaluated several LLMs and discovered that their proficiency in this aspect is limited, regardless of whether the knowledge is trained in a separate or adjacent training settings.
Extroversion or Introversion? Controlling The Personality of Your Large Language Models
Our investigation revealed a hierarchy of effectiveness in control: Prompt > SFT > RLHF > Continual Pre-train.
Getting More from Less: Large Language Models are Good Spontaneous Multilingual Learners
Recently, Large Language Models (LLMs) have shown impressive language capabilities.
Why Not Transform Chat Large Language Models to Non-English?
For the second issue, we propose a method comprising two synergistic components: low-rank adaptation for training to maintain the original LLM parameters, and recovery KD, which utilizes data generated by the chat LLM itself to recover the original knowledge from the frozen parameters.
The Power of Question Translation Training in Multilingual Reasoning: Broadened Scope and Deepened Insights
Bridging the significant gap between large language model's English and non-English performance presents a great challenge.
Improved Paraphrase Generation via Controllable Latent Diffusion
Paraphrase generation strives to generate high-quality and diverse expressions of a given text, a domain where diffusion models excel.
Multilingual Pretraining and Instruction Tuning Improve Cross-Lingual Knowledge Alignment, But Only Shallowly
In this paper, we propose CLiKA, a systematic framework to assess the cross-lingual knowledge alignment of LLMs in the Performance, Consistency and Conductivity levels, and explored the effect of multilingual pretraining and instruction tuning on the degree of alignment.
EDT: Improving Large Language Models' Generation by Entropy-based Dynamic Temperature Sampling
However, a fixed temperature parameter is used in most cases, which may not always be an optimal choice for balancing generation quality and diversity.
MT-PATCHER: Selective and Extendable Knowledge Distillation from Large Language Models for Machine Translation
Large Language Models (LLM) have demonstrated their strong ability in the field of machine translation (MT), yet they suffer from high computational cost and latency.
Measuring Meaning Composition in the Human Brain with Composition Scores from Large Language Models
Drawing on the key-value memory interpretation of transformer feed-forward network blocks, we introduce the Composition Score, a novel model-based metric designed to quantify the degree of meaning composition during sentence comprehension.
Diffusion Language Models Are Versatile Protein Learners
This paper introduces diffusion protein language model (DPLM), a versatile protein language model that demonstrates strong generative and predictive capabilities for protein sequences.
Cobra Effect in Reference-Free Image Captioning Metrics
If a certain metric has flaws, it will be exploited by the model and reflected in the generated sentences.
Question Translation Training for Better Multilingual Reasoning
A typical solution is to translate instruction data into all languages of interest, and then train on the resulting multilingual data, which is called translate-training.
Multi-Candidate Speculative Decoding
One way to speed them up is speculative decoding, which generates candidate segments (a sequence of tokens) from a fast draft model that is then verified in parallel by the target model.
MAPO: Advancing Multilingual Reasoning through Multilingual Alignment-as-Preference Optimization
To enhance reasoning abilities in non-dominant languages, we propose a Multilingual-Alignment-as-Preference Optimization framework (MAPO), aiming to align the reasoning processes in other languages with the dominant language.
Lost in the Source Language: How Large Language Models Evaluate the Quality of Machine Translation
This study investigates how Large Language Models (LLMs) leverage source and reference data in machine translation evaluation task, aiming to better understand the mechanisms behind their remarkable performance in this task.
A Wolf in Sheep's Clothing: Generalized Nested Jailbreak Prompts can Fool Large Language Models Easily
Finally, we analyze the failure of LLMs defense from the perspective of prompt execution priority, and propose corresponding defense strategies.
Exploring the Factual Consistency in Dialogue Comprehension of Large Language Models
For answering the factual questions, which is more challenging, the average error rate of all evaluated LLMs is 36. 1%.
Roles of Scaling and Instruction Tuning in Language Perception: Model vs. Human Attention
Recent large language models (LLMs) have revealed strong abilities to understand natural language.
IMTLab: An Open-Source Platform for Building, Evaluating, and Diagnosing Interactive Machine Translation Systems
We present IMTLab, an open-source end-to-end interactive machine translation (IMT) system platform that enables researchers to quickly build IMT systems with state-of-the-art models, perform an end-to-end evaluation, and diagnose the weakness of systems.
Dynamic Demonstrations Controller for In-Context Learning
Previous studies have revealed that ICL is sensitive to the selection and the ordering of demonstrations.
Only 5\% Attention Is All You Need: Efficient Long-range Document-level Neural Machine Translation
Document-level Neural Machine Translation (DocNMT) has been proven crucial for handling discourse phenomena by introducing document-level context information.
Unify word-level and span-level tasks: NJUNLP's Participation for the WMT2023 Quality Estimation Shared Task
We generate pseudo MQM data using parallel data from the WMT translation task.
Extrapolating Large Language Models to Non-English by Aligning Languages
We start from targeting individual languages by performing cross-lingual instruction-tuning (CoIT) on LLaMA, i. e. tuning it with translation task data and cross-lingual general task data to obtain cross-lingual models (x-LLaMAs), and formulate underlying scaling laws to investigate the advantages of using scalable translation data.
Food-500 Cap: A Fine-Grained Food Caption Benchmark for Evaluating Vision-Language Models
Experiments on our proposed datasets demonstrate that popular VLMs underperform in the food domain compared with their performance in the general domain.
BLEURT Has Universal Translations: An Analysis of Automatic Metrics by Minimum Risk Training
In this study, we systematically analyze and compare various mainstream and cutting-edge automatic metrics from the perspective of their guidance for training machine translation systems.
INK: Injecting kNN Knowledge in Nearest Neighbor Machine Translation
We propose an effective training framework INK to directly smooth the representation space via adjusting representations of kNN neighbors with a small number of new parameters.
Eliciting the Translation Ability of Large Language Models via Multilingual Finetuning with Translation Instructions
Secondly, we find that LLMs' ability to carry out translation instructions relies on the understanding of translation instructions and the alignment among different languages.
Multilingual Machine Translation with Large Language Models: Empirical Results and Analysis
Large language models (LLMs) have demonstrated remarkable potential in handling multilingual machine translation (MMT).
Selective Knowledge Distillation for Non-Autoregressive Neural Machine Translation
Benefiting from the sequence-level knowledge distillation, the Non-Autoregressive Transformer (NAT) achieves great success in neural machine translation tasks.
kNN-BOX: A Unified Framework for Nearest Neighbor Generation
Augmenting the base neural model with a token-level symbolic datastore is a novel generation paradigm and has achieved promising results in machine translation (MT).
Better Datastore, Better Translation: Generating Datastores from Pre-Trained Models for Nearest Neural Machine Translation
The effectiveness of kNNMT directly depends on the quality of retrieved neighbors.
CoP: Factual Inconsistency Detection by Controlling the Preference
To separate the preference for factual consistency, we propose an unsupervised framework named CoP by controlling the preference of the generation model with the help of prompt.
Helping the Weak Makes You Strong: Simple Multi-Task Learning Improves Non-Autoregressive Translators
Recently, non-autoregressive (NAR) neural machine translation models have received increasing attention due to their efficient parallel decoding.
What Knowledge Is Needed? Towards Explainable Memory for kNN-MT Domain Adaptation
kNN-MT presents a new paradigm for domain adaptation by building an external datastore, which usually saves all target language token occurrences in the parallel corpus.
Structure-Unified M-Tree Coding Solver for MathWord Problem
As one of the challenging NLP tasks, designing math word problem (MWP) solvers has attracted increasing research attention for the past few years.
Probing Cross-modal Semantics Alignment Capability from the Textual Perspective
In recent years, vision and language pre-training (VLP) models have advanced the state-of-the-art results in a variety of cross-modal downstream tasks.
Zero-shot Domain Adaptation for Neural Machine Translation with Retrieved Phrase-level Prompts
Domain adaptation is an important challenge for neural machine translation.
A Numerical Reasoning Question Answering System with Fine-grained Retriever and the Ensemble of Multiple Generators for FinQA
Here, we propose a numerical reasoning question answering system to answer numerical reasoning questions among financial text and table data sources, consisting of a retriever module, a generator module, and an ensemble module.
Analyzing the Intensity of Complaints on Social Media
Furthermore, we conduct a comprehensive linguistic analysis around complaints, including the connections between complaints and sentiment, and a cross-lingual comparison for complaints expressions used by Chinese and English speakers.
$\textit{latent}$-GLAT: Glancing at Latent Variables for Parallel Text Generation
Recently, parallel text generation has received widespread attention due to its success in generation efficiency.
Non-Parametric Online Learning from Human Feedback for Neural Machine Translation
We study the problem of online learning with human feedback in the human-in-the-loop machine translation, in which the human translators revise the machine-generated translations and then the corrected translations are used to improve the neural machine translation (NMT) system.
Learning Kernel-Smoothed Machine Translation with Retrieved Examples
How to effectively adapt neural machine translation (NMT) models according to emerging cases without retraining?
Non-Parametric Unsupervised Domain Adaptation for Neural Machine Translation
Recently, $k$NN-MT has shown the promising capability of directly incorporating the pre-trained neural machine translation (NMT) model with domain-specific token-level $k$-nearest-neighbor ($k$NN) retrieval to achieve domain adaptation without retraining.
When is Char Better Than Subword: A Systematic Study of Segmentation Algorithms for Neural Machine Translation
Subword segmentation algorithms have been a \textit{de facto} choice when building neural machine translation systems.
Energy-based Unknown Intent Detection with Data Manipulation
Unknown intent detection aims to identify the out-of-distribution (OOD) utterance whose intent has never appeared in the training set.
Adaptive Nearest Neighbor Machine Translation
On four benchmark machine translation datasets, we demonstrate that the proposed method is able to effectively filter out the noises in retrieval results and significantly outperforms the vanilla kNN-MT model.
DirectQE: Direct Pretraining for Machine Translation Quality Estimation
However, we argue that there are gaps between the predictor and the estimator in both data quality and training objectives, which preclude QE models from benefiting from a large number of parallel corpora more directly.
Duplex Sequence-to-Sequence Learning for Reversible Machine Translation
Thus REDER enables reversible machine translation by simply flipping the input and output ends.
Non-Autoregressive Translation by Learning Target Categorical Codes
Non-autoregressive Transformer is a promising text generation model.
Ranked #7 on
Machine Translation
on WMT2014 German-English
Dual Side Deep Context-aware Modulation for Social Recommendation
Specifically, we first proposed a novel graph neural network to model the social relation and collaborative relation, and on top of high-order relations, a dual side deep context-aware modulation is introduced to capture the friends' information and item attraction.
FGraDA: A Dataset and Benchmark for Fine-Grained Domain Adaptation in Machine Translation
Previous research for adapting a general neural machine translation (NMT) model into a specific domain usually neglects the diversity in translation within the same domain, which is a core problem for domain adaptation in real-world scenarios.
A Simple and Effective Approach to Robust Unsupervised Bilingual Dictionary Induction
Our experiments show that this simple method does not hamper the performance of similar language pairs and achieves an accuracy of 13. 64~55. 53% between English and four distant languages, i. e., Chinese, Japanese, Vietnamese and Thai.
Opinion Transmission Network for Jointly Improving Aspect-oriented Opinion Words Extraction and Sentiment Classification
In this paper, we propose a novel joint model, Opinion Transmission Network (OTN), to exploit the potential bridge between ALSC and AOWE to achieve the goal of facilitating them simultaneously.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+3
Transformer-based Multi-Aspect Modeling for Multi-Aspect Multi-Sentiment Analysis
To facilitate the research of ABSA, NLPCC 2020 Shared Task 2 releases a new large-scale Multi-Aspect Multi-Sentiment (MAMS) dataset.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+2
Rethinking Document-level Neural Machine Translation
This paper does not aim at introducing a novel model for document-level neural machine translation.
PNAT: Non-autoregressive Transformer by Position Learning
However, position modeling of output words is an essential problem in non-autoregressive text generation.
Non-linear Learning for Statistical Machine Translation
The linear combination assumes that all the features are in a linear relationship and constrains that each feature interacts with the rest features in an linear manner, which might limit the expressive power of the model and lead to a under-fit model on the current data.
