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Found 64 papers, 21 papers with code
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.
LLMCL-GEC: Advancing Grammatical Error Correction with LLM-Driven Curriculum Learning
While large-scale language models (LLMs) have demonstrated remarkable capabilities in specific natural language processing (NLP) tasks, they may still lack proficiency compared to specialized models in certain domains, such as grammatical error correction (GEC).
DetectRL: Benchmarking LLM-Generated Text Detection in Real-World Scenarios
More importantly, we analyzed the potential impact of writing styles, model types, attack methods, the text lengths, and real-world human writing factors on different types of detectors.
VisAidMath: Benchmarking Visual-Aided Mathematical Reasoning
Although previous research on large language models (LLMs) and large multi-modal models (LMMs) has systematically explored mathematical problem-solving (MPS) within visual contexts, the analysis of how these models process visual information during problem-solving remains insufficient.
What is the Best Way for ChatGPT to Translate Poetry?
Machine translation (MT) has historically faced significant challenges when applied to literary works, particularly in the domain of poetry translation.
FOCUS: Forging Originality through Contrastive Use in Self-Plagiarism for Language Models
Pre-trained Language Models (PLMs) have shown impressive results in various Natural Language Generation (NLG) tasks, such as powering chatbots and generating stories.
Who Wrote This? The Key to Zero-Shot LLM-Generated Text Detection Is GECScore
The efficacy of an large language model (LLM) generated text detector depends substantially on the availability of sizable training data.
A Two-Stage Prediction-Aware Contrastive Learning Framework for Multi-Intent NLU
Multi-intent natural language understanding (NLU) presents a formidable challenge due to the model confusion arising from multiple intents within a single utterance.
Ranked #2 on
Slot Filling
on MixSNIPS
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.
Prefix Text as a Yarn: Eliciting Non-English Alignment in Foundation Language Model
While supervised fine-tuning (SFT) has been a straightforward approach for tailoring the output of foundation large language model (LLM) to specific preferences, concerns have been raised about the depth of this alignment, with some critiques suggesting it is merely "superficial".
Let's Focus on Neuron: Neuron-Level Supervised Fine-tuning for Large Language Model
Large Language Models (LLMs) are composed of neurons that exhibit various behaviors and roles, which become increasingly diversified as models scale.
A Survey on LLM-Generated Text Detection: Necessity, Methods, and Future Directions
In this survey, we collate recent research breakthroughs in this area and underscore the pressing need to bolster detector research.
Human-in-the-loop Machine Translation with Large Language Model
The large language model (LLM) has garnered significant attention due to its in-context learning mechanisms and emergent capabilities.
Can LMs Generalize to Future Data? An Empirical Analysis on Text Summarization
Moreover, the knowledge memorized by PLMs may quickly become outdated, which affects the generalization performance of PLMs on future data.
Is ChatGPT a Highly Fluent Grammatical Error Correction System? A Comprehensive Evaluation
To showcase its capabilities in GEC, we design zero-shot chain-of-thought (CoT) and few-shot CoT settings using in-context learning for ChatGPT.
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.
Low Resource Neural Machine Translation
Low-Resource Neural Machine Translation
+4
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.
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.
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.
Variance-Aware Machine Translation Test Sets
We release 70 small and discriminative test sets for machine translation (MT) evaluation called variance-aware test sets (VAT), covering 35 translation directions from WMT16 to WMT20 competitions.
On the Complementarity between Pre-Training and Back-Translation for Neural Machine Translation
Pre-training (PT) and back-translation (BT) are two simple and powerful methods to utilize monolingual data for improving the model performance of neural machine translation (NMT).
Difficulty-Aware Machine Translation Evaluation
The high-quality translation results produced by machine translation (MT) systems still pose a huge challenge for automatic evaluation.
On the Copying Behaviors of Pre-Training for Neural Machine Translation
In response to this problem, we propose a simple and effective method named copying penalty to control the copying behaviors in decoding.
Meta-Curriculum Learning for Domain Adaptation in Neural Machine Translation
Meta-learning has been sufficiently validated to be beneficial for low-resource neural machine translation (NMT).
Domain Adaptation
Low Resource Neural Machine Translation
+4
Understanding and Improving Encoder Layer Fusion in Sequence-to-Sequence Learning
Encoder layer fusion (EncoderFusion) is a technique to fuse all the encoder layers (instead of the uppermost layer) for sequence-to-sequence (Seq2Seq) models, which has proven effective on various NLP tasks.
Document Graph for Neural Machine Translation
Previous works have shown that contextual information can improve the performance of neural machine translation (NMT).
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.
Norm-Based Curriculum Learning for Neural Machine Translation
We use the norm (aka length or module) of a word embedding as a measure of 1) the difficulty of the sentence, 2) the competence of the model, and 3) the weight of the sentence.
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.
Leveraging Local and Global Patterns for Self-Attention Networks
Self-attention networks have received increasing research attention.
Shared-Private Bilingual Word Embeddings for Neural Machine Translation
For similar source and target words, their embeddings tend to share a part of the features and they cooperatively learn these common representation units.
Learning Deep Transformer Models for Machine Translation
Transformer is the state-of-the-art model in recent machine translation evaluations.
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.
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
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.
UM-Corpus: A Large English-Chinese Parallel Corpus for Statistical Machine Translation
This paper describes the acquisition of a large scale and high quality parallel corpora for English and Chinese.
