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Found 59 papers, 25 papers with code
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.
RoBLEURT Submission for WMT2021 Metrics Task
After investigating the recent advances of trainable metrics, we conclude several aspects of vital importance to obtain a well-performed metric model by: 1) jointly leveraging the advantages of source-included model and reference-only model, 2) continuously pre-training the model with massive synthetic data pairs, and 3) fine-tuning the model with data denoising strategy.
GCPG: A General Framework for Controllable Paraphrase Generation
Under GCPG, we reconstruct commonly adopted lexical condition (i. e., Keywords) and syntactical conditions (i. e., Part-Of-Speech sequence, Constituent Tree, Masked Template and Sentential Exemplar) and study the combination of the two types.
Unsupervised Preference-Aware Language Identification
Recognizing the language of ambiguous texts has become a main challenge in language identification (LID).
Challenges of Neural Machine Translation for Short Texts
After empirically investigating the rationale behind this, we summarize two challenges in NMT for STs associated with translation error types above, respectively: (1) the imbalanced length distribution in training set intensifies model inference calibration over STs, leading to more over-translation cases on STs; and (2) the lack of contextual information forces NMT to have higher data uncertainty on short sentences, and thus NMT model is troubled by considerable mistranslation errors.
MinMo: A Multimodal Large Language Model for Seamless Voice Interaction
Previous models for voice interactions are categorized as native and aligned.
Qwen2.5 Technical Report
In addition, for hosted solutions, the proprietary models currently include two mixture-of-experts (MoE) variants: Qwen2. 5-Turbo and Qwen2. 5-Plus, both available from Alibaba Cloud Model Studio.
Ranked #7 on
on GPQA
P-MMEval: A Parallel Multilingual Multitask Benchmark for Consistent Evaluation of LLMs
Recent advancements in large language models (LLMs) showcase varied multilingual capabilities across tasks like translation, code generation, and reasoning.
ZhoBLiMP: a Systematic Assessment of Language Models with Linguistic Minimal Pairs in Chinese
Whether and how language models (LMs) acquire the syntax of natural languages has been widely evaluated under the minimal pair paradigm.
Not All Languages are Equal: Insights into Multilingual Retrieval-Augmented Generation
Experimental results reveal linguistic inequalities: 1) high-resource languages stand out in Monolingual Knowledge Extraction; 2) Indo-European languages lead RALMs to provide answers directly from documents, alleviating the challenge of expressing answers across languages; 3) English benefits from RALMs' selection bias and speaks louder in multilingual knowledge selection.
Latent Space Chain-of-Embedding Enables Output-free LLM Self-Evaluation
LLM self-evaluation relies on the LLM's own ability to estimate response correctness, which can greatly improve its deployment reliability.
Large Language Model for Multi-Domain Translation: Benchmarking and Domain CoT Fine-tuning
Achieving consistent high-quality machine translation (MT) across diverse domains remains a significant challenge, primarily due to the limited and imbalanced parallel training data available in various domains.
mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval
We first introduce a text encoder (base size) enhanced with RoPE and unpadding, pre-trained in a native 8192-token context (longer than 512 of previous multilingual encoders).
Qwen2 Technical Report
This report introduces the Qwen2 series, the latest addition to our large language models and large multimodal models.
Ranked #3 on
Arithmetic Reasoning
on GSM8K
(using extra training data)
MoE-CT: A Novel Approach For Large Language Models Training With Resistance To Catastrophic Forgetting
The advent of large language models (LLMs) has predominantly catered to high-resource languages, leaving a disparity in performance for low-resource languages.
AnyTrans: Translate AnyText in the Image with Large Scale Models
This paper introduces AnyTrans, an all-encompassing framework for the task-Translate AnyText in the Image (TATI), which includes multilingual text translation and text fusion within images.
Efficient k-Nearest-Neighbor Machine Translation with Dynamic Retrieval
In this paper, we first conduct a preliminary study to reveal two key limitations of $k$NN-MT-AR: 1) the optimization gap leads to inaccurate estimation of $\lambda$ for determining $k$NN retrieval skipping, and 2) using a fixed threshold fails to accommodate the dynamic demands for $k$NN retrieval at different timesteps.
Embedding Trajectory for Out-of-Distribution Detection in Mathematical Reasoning
Real-world data deviating from the independent and identically distributed (i. i. d.)
PolyLM: An Open Source Polyglot Large Language Model
Large language models (LLMs) demonstrate remarkable ability to comprehend, reason, and generate following nature language instructions.
Meta-Reasoning: Semantics-Symbol Deconstruction for Large Language Models
Neural-symbolic methods have demonstrated efficiency in enhancing the reasoning abilities of large language models (LLMs).
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.
From Statistical Methods to Deep Learning, Automatic Keyphrase Prediction: A Survey
In this paper, we comprehensively summarize representative studies from the perspectives of dominant models, datasets and evaluation metrics.
Towards Fine-Grained Information: Identifying the Type and Location of Translation Errors
In this paper, we propose Fine-Grained Translation Error Detection (FG-TED) task, aiming at identifying both the position and the type of translation errors on given source-hypothesis sentence pairs.
Competency-Aware Neural Machine Translation: Can Machine Translation Know its Own Translation Quality?
The lack of competency awareness makes NMT untrustworthy.
WR-ONE2SET: Towards Well-Calibrated Keyphrase Generation
Keyphrase generation aims to automatically generate short phrases summarizing an input document.
Alibaba-Translate China's Submission for WMT 2022 Metrics Shared Task
In this report, we present our submission to the WMT 2022 Metrics Shared Task.
Alibaba-Translate China's Submission for WMT 2022 Quality Estimation Shared Task
In this paper, we present our submission to the sentence-level MQM benchmark at Quality Estimation Shared Task, named UniTE (Unified Translation Evaluation).
Draft, Command, and Edit: Controllable Text Editing in E-Commerce
To address this challenge, we explore a new draft-command-edit manner in description generation, leading to the proposed new task-controllable text editing in E-commerce.
Should We Rely on Entity Mentions for Relation Extraction? Debiasing Relation Extraction with Counterfactual Analysis
In this paper, we propose the CORE (Counterfactual Analysis based Relation Extraction) debiasing method that guides the RE models to focus on the main effects of textual context without losing the entity information.
Dangling-Aware Entity Alignment with Mixed High-Order Proximities
We study dangling-aware entity alignment in knowledge graphs (KGs), which is an underexplored but important problem.
Attention Mechanism with Energy-Friendly Operations
Attention mechanism has become the dominant module in natural language processing models.
UniTE: Unified Translation Evaluation
Translation quality evaluation plays a crucial role in machine translation.
RoBLEURT Submission for the WMT2021 Metrics Task
After investigating the recent advances of trainable metrics, we conclude several aspects of vital importance to obtain a well-performed metric model by: 1) jointly leveraging the advantages of source-included model and reference-only model, 2) continuously pre-training the model with massive synthetic data pairs, and 3) fine-tuning the model with data denoising strategy.
Tailor: A Prompt-Based Approach to Attribute-Based Controlled Text Generation
We experimentally find that these prompts can be simply concatenated as a whole to multi-attribute CTG without any re-training, yet raises problems of fluency decrease and position sensitivity.
RMBR: A Regularized Minimum Bayes Risk Reranking Framework for Machine Translation
Recently, Minimum Bayes Risk (MBR) decoding has been proposed to improve the quality for NMT, which seeks for a consensus translation that is closest on average to other candidates from the n-best list.
Frequency-Aware Contrastive Learning for Neural Machine Translation
Inspired by the observation that low-frequency words form a more compact embedding space, we tackle this challenge from a representation learning perspective.
KGR^4: Retrieval, Retrospect, Refine and Rethink for Commonsense Generation
Generative commonsense reasoning requires machines to generate sentences describing an everyday scenario given several concepts, which has attracted much attention recently.
Leveraging Advantages of Interactive and Non-Interactive Models for Vector-Based Cross-Lingual Information Retrieval
Interactive and non-interactive model are the two de-facto standard frameworks in vector-based cross-lingual information retrieval (V-CLIR), which embed queries and documents in synchronous and asynchronous fashions, respectively.
Computational Efficiency
Cross-Lingual Information Retrieval
+5
Bridging Subword Gaps in Pretrain-Finetune Paradigm for Natural Language Generation
A well-known limitation in pretrain-finetune paradigm lies in its inflexibility caused by the one-size-fits-all vocabulary.
Towards User-Driven Neural Machine Translation
Furthermore, we contribute the first Chinese-English parallel corpus annotated with user behavior called UDT-Corpus.
Multi-Hop Transformer for Document-Level Machine Translation
Document-level neural machine translation (NMT) has proven to be of profound value for its effectiveness on capturing contextual information.
Domain Transfer based Data Augmentation for Neural Query Translation
Query translation (QT) serves as a critical factor in successful cross-lingual information retrieval (CLIR).
Constraint Translation Candidates: A Bridge between Neural Query Translation and Cross-lingual Information Retrieval
Query translation (QT) is a key component in cross-lingual information retrieval system (CLIR).
Exploiting Neural Query Translation into Cross Lingual Information Retrieval
As a crucial role in cross-language information retrieval (CLIR), query translation has three main challenges: 1) the adequacy of translation; 2) the lack of in-domain parallel training data; and 3) the requisite of low latency.
Self-Paced Learning for Neural Machine Translation
Recent studies have proven that the training of neural machine translation (NMT) can be facilitated by mimicking the learning process of humans.
Uncertainty-Aware Curriculum Learning for Neural Machine Translation
We propose uncertainty-aware curriculum learning, which is motivated by the intuition that: 1) the higher the uncertainty in a translation pair, the more complex and rarer the information it contains; and 2) the end of the decline in model uncertainty indicates the completeness of current training stage.
Unsupervised Neural Dialect Translation with Commonality and Diversity Modeling
As a special machine translation task, dialect translation has two main characteristics: 1) lack of parallel training corpus; and 2) possessing similar grammar between two sides of the translation.
Neuron Interaction Based Representation Composition for Neural Machine Translation
Starting from this intuition, we propose a novel approach to compose representations learned by different components in neural machine translation (e. g., multi-layer networks or multi-head attention), based on modeling strong interactions among neurons in the representation vectors.
Leveraging Local and Global Patterns for Self-Attention Networks
Self-attention networks have received increasing research attention.
Assessing the Ability of Self-Attention Networks to Learn Word Order
Self-attention networks (SAN) have attracted a lot of interests due to their high parallelization and strong performance on a variety of NLP tasks, e. g. machine translation.
Convolutional Self-Attention Networks
Self-attention networks (SANs) have drawn increasing interest due to their high parallelization in computation and flexibility in modeling dependencies.
Information Aggregation for Multi-Head Attention with Routing-by-Agreement
Multi-head attention is appealing for its ability to jointly extract different types of information from multiple representation subspaces.
Modeling Recurrence for Transformer
In addition to the standard recurrent neural network, we introduce a novel attentive recurrent network to leverage the strengths of both attention and recurrent networks.
Context-Aware Self-Attention Networks
Self-attention model have shown its flexibility in parallel computation and the effectiveness on modeling both long- and short-term dependencies.
Convolutional Self-Attention Network
Self-attention network (SAN) has recently attracted increasing interest due to its fully parallelized computation and flexibility in modeling dependencies.
Modeling Localness for Self-Attention Networks
Self-attention networks have proven to be of profound value for its strength of capturing global dependencies.
Ranked #28 on
Machine Translation
on WMT2014 English-German
Multi-Head Attention with Disagreement Regularization
Multi-head attention is appealing for the ability to jointly attend to information from different representation subspaces at different positions.
Towards Bidirectional Hierarchical Representations for Attention-Based Neural Machine Translation
This paper proposes a hierarchical attentional neural translation model which focuses on enhancing source-side hierarchical representations by covering both local and global semantic information using a bidirectional tree-based encoder.
