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Found 33 papers, 20 papers with code
Adaptive Bridge between Training and Inference for Dialogue Generation
Although exposure bias has been widely studied in some NLP tasks, it faces its unique challenges in dialogue response generation, the representative one-to-various generation scenario. In real human dialogue, there are many appropriate responses for the same context, not only with different expressions, but also with different topics.
Narrowing the Gap between Zero- and Few-shot Machine Translation by Matching Styles
Large language models trained primarily in a monolingual setting have demonstrated their ability to generalize to machine translation using zero- and few-shot examples with in-context learning.
Error Norm Truncation: Robust Training in the Presence of Data Noise for Text Generation Models
Text generation models are notoriously vulnerable to errors in the training data.
A Paradigm Shift in Machine Translation: Boosting Translation Performance of Large Language Models
In this study, we propose a novel fine-tuning approach for LLMs that is specifically designed for the translation task, eliminating the need for the abundant parallel data that traditional translation models usually depend on.
Offline Multi-Agent Reinforcement Learning with Implicit Global-to-Local Value Regularization
Offline reinforcement learning (RL) has received considerable attention in recent years due to its attractive capability of learning policies from offline datasets without environmental interactions.
Offline Reinforcement Learning with Imbalanced Datasets
The prevalent use of benchmarks in current offline reinforcement learning (RL) research has led to a neglect of the imbalance of real-world dataset distributions in the development of models.
PROTO: Iterative Policy Regularized Offline-to-Online Reinforcement Learning
Offline-to-online reinforcement learning (RL), by combining the benefits of offline pretraining and online finetuning, promises enhanced sample efficiency and policy performance.
Condensing Multilingual Knowledge with Lightweight Language-Specific Modules
Incorporating language-specific (LS) modules is a proven method to boost performance in multilingual machine translation.
Towards Being Parameter-Efficient: A Stratified Sparsely Activated Transformer with Dynamic Capacity
Mixture-of-experts (MoE) models that employ sparse activation have demonstrated effectiveness in significantly increasing the number of parameters while maintaining low computational requirements per token.
Offline RL with No OOD Actions: In-Sample Learning via Implicit Value Regularization
This gives a deeper understanding of why the in-sample learning paradigm works, i. e., it applies implicit value regularization to the policy.
Language-Aware Multilingual Machine Translation with Self-Supervised Learning
In this work, we first investigate how to utilize intra-distillation to learn more *language-specific* parameters and then show the importance of these language-specific parameters.
Mind the Gap: Offline Policy Optimization for Imperfect Rewards
RGM is formulated as a bi-level optimization problem: the upper layer optimizes a reward correction term that performs visitation distribution matching w. r. t.
SaFormer: A Conditional Sequence Modeling Approach to Offline Safe Reinforcement Learning
Offline safe RL is of great practical relevance for deploying agents in real-world applications.
Simoun: Synergizing Interactive Motion-appearance Understanding for Vision-based Reinforcement Learning
Efficient motion and appearance modeling are critical for vision-based Reinforcement Learning (RL).
A Policy-Guided Imitation Approach for Offline Reinforcement Learning
We decompose the conventional reward-maximizing policy in offline RL into a guide-policy and an execute-policy.
Discriminator-Weighted Offline Imitation Learning from Suboptimal Demonstrations
We study the problem of offline Imitation Learning (IL) where an agent aims to learn an optimal expert behavior policy without additional online environment interactions.
Discriminator-Guided Model-Based Offline Imitation Learning
In this paper, we propose the Discriminator-guided Model-based offline Imitation Learning (DMIL) framework, which introduces a discriminator to simultaneously distinguish the dynamics correctness and suboptimality of model rollout data against real expert demonstrations.
The Importance of Being Parameters: An Intra-Distillation Method for Serious Gains
We first highlight the large sensitivity (contribution) gap among high-sensitivity and low-sensitivity parameters and show that the model generalization performance can be significantly improved after balancing the contribution of all parameters.
When Data Geometry Meets Deep Function: Generalizing Offline Reinforcement Learning
In offline reinforcement learning (RL), one detrimental issue to policy learning is the error accumulation of deep Q function in out-of-distribution (OOD) areas.
Por Qué Não Utiliser Alla Språk? Mixed Training with Gradient Optimization in Few-Shot Cross-Lingual Transfer
The current state-of-the-art for few-shot cross-lingual transfer learning first trains on abundant labeled data in the source language and then fine-tunes with a few examples on the target language, termed target-adapting.
VAE based Text Style Transfer with Pivot Words Enhancement Learning
Text Style Transfer (TST) aims to alter the underlying style of the source text to another specific style while keeping the same content.
Adaptive Bridge between Training and Inference for Dialogue
Although exposure bias has been widely studied in some NLP tasks, it faces its unique challenges in dialogue response generation, the representative one-to-various generation scenario.
Offline Reinforcement Learning with Soft Behavior Regularization
In this work, we start from the performance difference between the learned policy and the behavior policy, we derive a new policy learning objective that can be used in the offline setting, which corresponds to the advantage function value of the behavior policy, multiplying by a state-marginal density ratio.
Enhancing semi-supervised learning via self-interested coalitional learning
Jointly solving these two tasks allows full utilization of information from both labeled and unlabeled data, thus alleviating the problem of over-reliance on labeled data.
Everything Is All It Takes: A Multipronged Strategy for Zero-Shot Cross-Lingual Information Extraction
Zero-shot cross-lingual information extraction (IE) describes the construction of an IE model for some target language, given existing annotations exclusively in some other language, typically English.
BERT, mBERT, or BiBERT? A Study on Contextualized Embeddings for Neural Machine Translation
The success of bidirectional encoders using masked language models, such as BERT, on numerous natural language processing tasks has prompted researchers to attempt to incorporate these pre-trained models into neural machine translation (NMT) systems.
Ranked #2 on
Machine Translation
on IWSLT2014 German-English
Constraints Penalized Q-learning for Safe Offline Reinforcement Learning
We study the problem of safe offline reinforcement learning (RL), the goal is to learn a policy that maximizes long-term reward while satisfying safety constraints given only offline data, without further interaction with the environment.
Cross-Lingual BERT Contextual Embedding Space Mapping with Isotropic and Isometric Conditions
Typically, a linearly orthogonal transformation mapping is learned by aligning static type-level embeddings to build a shared semantic space.
Model-Based Offline Planning with Trajectory Pruning
The recent offline reinforcement learning (RL) studies have achieved much progress to make RL usable in real-world systems by learning policies from pre-collected datasets without environment interaction.
Zero-Shot Cross-Lingual Dependency Parsing through Contextual Embedding Transformation
Linear embedding transformation has been shown to be effective for zero-shot cross-lingual transfer tasks and achieve surprisingly promising results.
Gradual Fine-Tuning for Low-Resource Domain Adaptation
Fine-tuning is known to improve NLP models by adapting an initial model trained on more plentiful but less domain-salient examples to data in a target domain.
DeepThermal: Combustion Optimization for Thermal Power Generating Units Using Offline Reinforcement Learning
Optimizing the combustion efficiency of a thermal power generating unit (TPGU) is a highly challenging and critical task in the energy industry.
Meet Changes with Constancy: Learning Invariance in Multi-Source Translation
Multi-source neural machine translation aims to translate from parallel sources of information (e. g. languages, images, etc.)
