Papers with sequence generation tasks
Non-Autoregressive Sequence Generation (2022.acl-tutorials)
Copied to clipboard
| Challenge: | Non-autoregressive sequence generation (NAR) models generate output sequences in parallel to speed up generation process. |
| Approach: | This tutorial provides a thorough introduction and review of non-autoregressive sequence generation . it aims to generate the entire or partial output sequences in parallel to speed up the generation process . |
| Outcome: | This tutorial provides a thorough introduction and review of non-autoregressive sequence generation . it aims to reduce the performance gap between state-of-the-art models due to lack of modeling power . |
Non-Autoregressive Models for Fast Sequence Generation (2022.emnlp-tutorials)
Copied to clipboard
| Challenge: | Autoregressive (AR) models can only generate target sequence word-by-word due to the AR mechanism and suffer from slow inference. |
| Approach: | This tutorial provides an introduction to non-autoregressive sequence generation. |
| Outcome: | This tutorial explains how to generate non-autoregressive sequence generation models. |
Linearizing Transformer with Key-Value Memory (2022.emnlp-main)
Copied to clipboard
| Challenge: | Efficient transformer variants with linear time complexity have been developed to mitigate the quadratic computational overhead of the vanilla transformer. |
| Approach: | They propose a linear time complexity transformer variant that reduces the quadratic computational overhead of the vanilla transformer by using a recurrent-style incremental computation similar to kernel-based transformers. |
| Outcome: | The proposed method reduces the performance gap while achieving the same efficiency even with short generation. |
Advancing Sequential Numerical Prediction in Autoregressive Models (2025.acl-short)
Copied to clipboard
| Challenge: | Autoregressive models are the de facto choice for sequence generation tasks, but standard approaches treat digits as independent tokens and apply cross-entropy loss, overlooking the coherent structure of numerical sequences. |
| Approach: | They propose a novel approach to entropy loss by extending the Earth Mover’s Distance to preserve ordinal relationships between numerical values and sequence-level to penalize the overall discrepancy between predicted and actual sequences. |
| Outcome: | Extensive experiments show that NTIL improves numerical prediction and integrates effectively with LLMs/MLLMs. |
Conditional Poisson Stochastic Beams (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for beam search are based on a deterministic approach, but the results are not as accurate as those used in SBS. |
| Approach: | They propose a method that turns beam search into a stochastic process by using conditional Poisson sampling design instead of taking the maximizing set at each iteration. |
| Outcome: | The proposed method produces lower variance and more efficient estimators than SBS, even showing improvements in high entropy settings. |
Beyond Error Propagation in Neural Machine Translation: Characteristics of Language Also Matter (D18-1)
Copied to clipboard
| Challenge: | Neural machine translation suffers from exposure bias and error propagation problem. |
| Approach: | They conduct a series of analyses to deeply understand the accuracy drop problem . they find that the left part of the translated sentence is often better than its right part . |
| Outcome: | The results show that the left part of the translated sentence is often better than its right part in left-to-right decoding models. |
Transfer Learning for Sequence Generation: from Single-source to Multi-source (2021.acl-long)
Copied to clipboard
| Challenge: | Recent studies have shown that pretrained models are effective for low-resource downstream tasks. |
| Approach: | They propose a two-stage finetuning method to transfer pretrained models to MSG tasks by concatenating multiple sources into a single long sequence. |
| Outcome: | The proposed model outperforms baselines on the WMT17 APE task and multi-source translation task using the WTM14 test set. |
From RAG to Riches: Retrieval Interlaced with Sequence Generation (2024.emnlp-main)
Copied to clipboard
| Challenge: | RICHES interleaves retrieval with sequence generation tasks . traditional approaches chain LLM generation with separate retrieval model . |
| Approach: | They propose a novel approach that interleaves retrieval with sequence generation tasks . they propose attributed evidence, multi-hop retrievals and interleave thoughts to plan on what to retrieve next . |
| Outcome: | The proposed approach can work with any Instruction-tuned model, without additional training. |
Enhancing Hindi Feature Representation through Fusion of Dual-Script Word Embeddings (2024.lrec-main)
Copied to clipboard
| Challenge: | Pretrained language models often neglect the integration of different scripts within a language, constraining their ability to capture richer semantic information. |
| Approach: | They propose a dual-script enhanced feature representation method for Hindi . they combine features from Devanagari and Romanized Hindi Roberta . |
| Outcome: | The proposed method improves model performance across multiple natural language processing tasks. |
JANUS: Joint Autoregressive and Non-autoregressive Training with Auxiliary Loss for Sequence Generation (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to train autoregressive and non-autoregressive models only consider relevance of model parameters, ignoring correlations between the two manners. |
| Approach: | They propose a joint autoregressive and non-autoregressive training method using aUxiliary losS to enhance the model performance in both AR and NAR manners simultaneously. |
| Outcome: | The proposed method improves the model performance in both AR and NAR manners and reduces the inference latency. |
Instruction Position Matters in Sequence Generation with Large Language Models (2024.findings-acl)
Copied to clipboard
| Challenge: | Large language models (LLMs) can perform conditional sequence generation tasks, such as translation or summarization, through instruction fine-tuning. |
| Approach: | They propose to shift the position of task instructions after the input sentences to enhance the model's instruction-following capability. |
| Outcome: | The proposed method outperforms traditional settings across various model scales (1B / 7B & 13B) and different sequence generation tasks (translation and summarization) without any additional data or annotation costs. |
On the Evaluation of Speech Foundation Models for Spoken Language Understanding (2024.findings-acl)
Copied to clipboard
Siddhant Arora, Ankita Pasad, Chung-Ming Chien, Jionghao Han, Roshan Sharma, Jee-weon Jung, Hira Dhamyal, William Chen, Suwon Shon, Hung-yi Lee, Karen Livescu, Shinji Watanabe
| Challenge: | Spoken language understanding evaluation (SLUE) benchmarks are used to benchmark complex spoken language understanding tasks on natural speech. |
| Approach: | They propose a set of benchmark tasks to evaluate spoken language understanding on natural speech . they use pre-trained speech foundation models to evaluate the utility of different SFMs . |
| Outcome: | The proposed framework outperforms pre-trained speech foundation models on natural speech . the proposed framework also outperformed self-supervised SFMs on the sequence generation tasks . |
PLaD: Preference-based Large Language Model Distillation with Pseudo-Preference Pairs (2024.findings-acl)
Copied to clipboard
Rongzhi Zhang, Jiaming Shen, Tianqi Liu, Haorui Wang, Zhen Qin, Feng Han, Jialu Liu, Simon Baumgartner, Michael Bendersky, Chao Zhang
| Challenge: | Knowledge distillation (KD) is a technique for transferring expertise from large teacher models to compact student models with reduced memory footprints and inference costs. |
| Approach: | They propose to transfer knowledge from large teacher models to compact student models by exploiting teacher-student capacity discrepancies to generate pseudo-preference pairs where teacher outputs are preferred over student outputs. |
| Outcome: | The proposed framework exploits teacher-student capacity discrepancy to generate pseudo-preference pairs where teacher outputs are preferred over student outputs. |