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Found 42 papers, 19 papers with code
Learning to Rewrite for Non-Autoregressive Neural Machine Translation
Towards keeping the consistency of data distribution with iterative decoding, an iterative training strategy is employed to further improve the capacity of rewriting.
MSAMSum: Towards Benchmarking Multi-lingual Dialogue Summarization
Dialogue summarization helps users capture salient information from various types of dialogues has received much attention recently.
Improving Controllable Text Generation with Position-Aware Weighted Decoding
Weighted decoding methods composed of the pretrained language model (LM) and the controller have achieved promising results for controllable text generation.
Aligning Translation-Specific Understanding to General Understanding in Large Language Models
To align the translation-specific understanding to the general one, we propose a novel translation process xIoD (Cross-Lingual Interpretation of Difficult words), explicitly incorporating the general understanding on the content incurring inconsistent understanding to guide the translation.
Length Extrapolation of Transformers: A Survey from the Perspective of Positional Encoding
In this survey, we present these advances towards length extrapolation in a unified notation from the perspective of PE.
Emage: Non-Autoregressive Text-to-Image Generation
Compared with autoregressive baselines that needs to run one thousand times, our model only runs 16 times to generate images of competitive quality with an order of magnitude lower inference latency.
Trends in Integration of Knowledge and Large Language Models: A Survey and Taxonomy of Methods, Benchmarks, and Applications
In this paper, we propose a review to discuss the trends in integration of knowledge and large language models, including taxonomy of methods, benchmarks, and applications.
A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions
The emergence of large language models (LLMs) has marked a significant breakthrough in natural language processing (NLP), leading to remarkable advancements in text understanding and generation.
Retrieval-Generation Synergy Augmented Large Language Models
Large language models augmented with task-relevant documents have demonstrated impressive performance on knowledge-intensive tasks.
Adapter-based Selective Knowledge Distillation for Federated Multi-domain Meeting Summarization
However, existing work has focused on training models on centralized data, neglecting real-world scenarios where meeting data are infeasible to collect centrally, due to their sensitive nature.
SkillNet-X: A Multilingual Multitask Model with Sparsely Activated Skills
Traditional multitask learning methods basically can only exploit common knowledge in task- or language-wise, which lose either cross-language or cross-task knowledge.
Improved Visual Story Generation with Adaptive Context Modeling
Diffusion models developed on top of powerful text-to-image generation models like Stable Diffusion achieve remarkable success in visual story generation.
Towards Higher Pareto Frontier in Multilingual Machine Translation
Multilingual neural machine translation has witnessed remarkable progress in recent years.
The Role of Summarization in Generative Agents: A Preliminary Perspective
Generative agents that simulate human society show tremendous potential for further research and practical applications.
Hierarchical Catalogue Generation for Literature Review: A Benchmark
Scientific literature review generation aims to extract and organize important information from an abundant collection of reference papers and produces corresponding reviews while lacking a clear and logical hierarchy.
STOA-VLP: Spatial-Temporal Modeling of Object and Action for Video-Language Pre-training
Although large-scale video-language pre-training models, which usually build a global alignment between the video and the text, have achieved remarkable progress on various downstream tasks, the idea of adopting fine-grained information during the pre-training stage is not well explored.
Semantic-aware Contrastive Learning for Electroencephalography-to-Text Generation with Curriculum Learning
To mitigate this challenge, we devise a Curriculum Semantic-aware Contrastive Learning strategy (C-SCL), which effectively re-calibrates the subject-dependent EEG representation to the semantic-dependent EEG representation, thus reducing the discrepancy.
Controllable Text Generation via Probability Density Estimation in the Latent Space
Previous work on controllable text generation has explored the idea of control from the latent space, such as optimizing a representation with attribute-related classifiers or sampling a representation from relevant discrete samples.
A Distributional Lens for Multi-Aspect Controllable Text Generation
Multi-aspect controllable text generation is a more challenging and practical task than single-aspect control.
Unifying the Convergences in Multilingual Neural Machine Translation
In this paper, we propose a novel training strategy named LSSD (Language-Specific Self-Distillation), which can alleviate the convergence inconsistency and help MNMT models achieve the best performance on each language pair simultaneously.
Pretraining without Wordpieces: Learning Over a Vocabulary of Millions of Words
(2) how to predict a word via cloze test without knowing the number of wordpieces in advance?
A Survey on Dialogue Summarization: Recent Advances and New Frontiers
We hope that this first survey of dialogue summarization can provide the community with a quick access and a general picture to this task and motivate future researches.
Language Model as an Annotator: Exploring DialoGPT for Dialogue Summarization
Current dialogue summarization systems usually encode the text with a number of general semantic features (e. g., keywords and topics) to gain more powerful dialogue modeling capabilities.
The Factual Inconsistency Problem in Abstractive Text Summarization: A Survey
Recently, various neural encoder-decoder models pioneered by Seq2Seq framework have been proposed to achieve the goal of generating more abstractive summaries by learning to map input text to output text.
Dialogue Discourse-Aware Graph Model and Data Augmentation for Meeting Summarization
First, we present a Dialogue Discourse-Dware Meeting Summarizer (DDAMS) to explicitly model the interaction between utterances in a meeting by modeling different discourse relations.
TableGPT: Few-shot Table-to-Text Generation with Table Structure Reconstruction and Content Matching
Although neural table-to-text models have achieved remarkable progress with the help of large-scale datasets, they suffer insufficient learning problem with limited training data.
Enhancing Content Planning for Table-to-Text Generation with Data Understanding and Verification
Neural table-to-text models, which select and order salient data, as well as verbalizing them fluently via surface realization, have achieved promising progress.
Incorporating Commonsense Knowledge into Abstractive Dialogue Summarization via Heterogeneous Graph Networks
In detail, we consider utterance and commonsense knowledge as two different types of data and design a Dialogue Heterogeneous Graph Network (D-HGN) for modeling both information.
Learning to Select Bi-Aspect Information for Document-Scale Text Content Manipulation
In this paper, we focus on a new practical task, document-scale text content manipulation, which is the opposite of text style transfer and aims to preserve text styles while altering the content.
CodeBERT: A Pre-Trained Model for Programming and Natural Languages
Results show that CodeBERT achieves state-of-the-art performance on both natural language code search and code documentation generation tasks.
Ranked #1 on
Code Documentation Generation
on CodeSearchNet - Go
Enhancing Neural Data-To-Text Generation Models with External Background Knowledge
Recent neural models for data-to-text generation rely on massive parallel pairs of data and text to learn the writing knowledge.
Neural Semantic Parsing in Low-Resource Settings with Back-Translation and Meta-Learning
Neural semantic parsing has achieved impressive results in recent years, yet its success relies on the availability of large amounts of supervised data.
Table-to-Text Generation with Effective Hierarchical Encoder on Three Dimensions (Row, Column and Time)
To address aforementioned problems, not only do we model each table cell considering other records in the same row, we also enrich table's representation by modeling each table cell in context of other cells in the same column or with historical (time dimension) data respectively.
Adaptive Multi-pass Decoder for Neural Machine Translation
Although end-to-end neural machine translation (NMT) has achieved remarkable progress in the recent years, the idea of adopting multi-pass decoding mechanism into conventional NMT is not well explored.
Knowledge-Aware Conversational Semantic Parsing Over Web Tables
Results show that our knowledge-aware model outperforms the state-of-the-art approaches.
Knowledge Based Machine Reading Comprehension
Machine reading comprehension (MRC) requires reasoning about both the knowledge involved in a document and knowledge about the world.
Semantic Parsing with Syntax- and Table-Aware SQL Generation
We present a generative model to map natural language questions into SQL queries.
Ranked #4 on
Code Generation
on WikiSQL
English-Chinese Knowledge Base Translation with Neural Network
Knowledge base (KB) such as Freebase plays an important role for many natural language processing tasks.
Bitext Name Tagging for Cross-lingual Entity Annotation Projection
Instead of directly relying on word alignment results, this framework combines advantages of rule-based methods and deep learning methods by implementing two steps: First, generates a high-confidence entity annotation set on IL side with strict searching methods; Second, uses this high-confidence set to weakly supervise the model training.
Effective LSTMs for Target-Dependent Sentiment Classification
Target-dependent sentiment classification remains a challenge: modeling the semantic relatedness of a target with its context words in a sentence.
Aspect-Based Sentiment Analysis (ABSA)
General Classification
+2
