Search Results for author:
Found 19 papers, 6 papers with code
A Thorough Evaluation of Task-Specific Pretraining for Summarization
Task-agnostic pretraining objectives like masked language models or corrupted span prediction are applicable to a wide range of NLP downstream tasks (Raffel et al., 2019), but are outperformed by task-specific pretraining objectives like predicting extracted gap sentences on summarization (Zhang et al., 2020).
LM-CPPF: Paraphrasing-Guided Data Augmentation for Contrastive Prompt-Based Few-Shot Fine-Tuning
This paper proposes LM-CPPF, Contrastive Paraphrasing-guided Prompt-based Fine-tuning of Language Models, which leverages prompt-based few-shot paraphrasing using generative language models, especially large language models such as GPT-3 and OPT-175B, for data augmentation.
Ranked #2 on
Sentiment Analysis
on CR
Zero-Shot Retrieval with Search Agents and Hybrid Environments
We extend the previous learning to search setup to a hybrid environment, which accepts discrete query refinement operations, after a first-pass retrieval step via a dual encoder.
Boosting Search Engines with Interactive Agents
This paper presents first successful steps in designing search agents that learn meta-strategies for iterative query refinement in information-seeking tasks.
A Simple Recipe for Multilingual Grammatical Error Correction
This paper presents a simple recipe to train state-of-the-art multilingual Grammatical Error Correction (GEC) models.
Ranked #1 on
Grammatical Error Correction
on Falko-MERLIN
(using extra training data)
Focus Attention: Promoting Faithfulness and Diversity in Summarization
Professional summaries are written with document-level information, such as the theme of the document, in mind.
Unsupervised Text Style Transfer with Padded Masked Language Models
This allows us to identify the source tokens to delete to transform the source text to match the style of the target domain.
A Generative Approach to Titling and Clustering Wikipedia Sections
We evaluate the performance of transformer encoders with various decoders for information organization through a new task: generation of section headings for Wikipedia articles.
Encode, Tag, Realize: High-Precision Text Editing
We propose LaserTagger - a sequence tagging approach that casts text generation as a text editing task.
Ranked #1 on
Sentence Fusion
on DiscoFuse
Leveraging Pre-trained Checkpoints for Sequence Generation Tasks
Unsupervised pre-training of large neural models has recently revolutionized Natural Language Processing.
Ranked #1 on
Split and Rephrase
on WikiSplit
Sentence-Level Fluency Evaluation: References Help, But Can Be Spared!
Motivated by recent findings on the probabilistic modeling of acceptability judgments, we propose syntactic log-odds ratio (SLOR), a normalized language model score, as a metric for referenceless fluency evaluation of natural language generation output at the sentence level.
Learning to Learn from Weak Supervision by Full Supervision
In this paper, we propose a method for training neural networks when we have a large set of data with weak labels and a small amount of data with true labels.
Avoiding Your Teacher's Mistakes: Training Neural Networks with Controlled Weak Supervision
Thus we avoid that the weight updates computed from noisy labels harm the quality of the target network model.
AutoExtend: Combining Word Embeddings with Semantic Resources
We present AutoExtend, a system that combines word embeddings with semantic resources by learning embeddings for non-word objects like synsets and entities and learning word embeddings that incorporate the semantic information from the resource.
Learning to Attend, Copy, and Generate for Session-Based Query Suggestion
e results suggest that our model outperforms the baselines both in terms of the generating queries and scoring candidate queries for the task of query suggestion.
Ultradense Word Embeddings by Orthogonal Transformation
Embeddings are generic representations that are useful for many NLP tasks.
AutoExtend: Extending Word Embeddings to Embeddings for Synsets and Lexemes
We present \textit{AutoExtend}, a system to learn embeddings for synsets and lexemes.
