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Found 13 papers, 5 papers with code
Zero-Shot Crosslingual Sentence Simplification
Sentence simplification aims to make sentences easier to read and understand.
Teaching Small Language Models to Reason
Chain of thought prompting successfully improves the reasoning capabilities of large language models, achieving state of the art results on a range of datasets.
Text Generation with Text-Editing Models
Text-editing models have recently become a prominent alternative to seq2seq models for monolingual text-generation tasks such as grammatical error correction, simplification, and style transfer.
EdiT5: Semi-Autoregressive Text-Editing with T5 Warm-Start
This is achieved by decomposing the generation process into three sub-tasks: (1) tagging to decide on the subset of input tokens to be preserved in the output, (2) re-ordering to define their order in the output text, and (3) insertion to infill the missing tokens that are not present in the input.
RED-ACE: Robust Error Detection for ASR using Confidence Embeddings
ASR Error Detection (AED) models aim to post-process the output of Automatic Speech Recognition (ASR) systems, in order to detect transcription errors.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
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)
Felix: Flexible Text Editing Through Tagging and Insertion
We achieve this by decomposing the text-editing task into two sub-tasks: tagging to decide on the subset of input tokens and their order in the output text and insertion to in-fill the missing tokens in the output not present in the input.
University of Edinburgh's submission to the Document-level Generation and Translation Shared Task
The University of Edinburgh participated in all six tracks: NLG, MT, and MT+NLG with both English and German as targeted languages.
Controllable Sentence Simplification: Employing Syntactic and Lexical Constraints
Sentence simplification aims to make sentences easier to read and understand.
Sentence Compression for Arbitrary Languages via Multilingual Pivoting
In this paper we advocate the use of bilingual corpora which are abundantly available for training sentence compression models.
Learning to Paraphrase for Question Answering
Question answering (QA) systems are sensitive to the many different ways natural language expresses the same information need.
Learning Paraphrastic Sentence Embeddings from Back-Translated Bitext
We consider the problem of learning general-purpose, paraphrastic sentence embeddings in the setting of Wieting et al. (2016b).
Paraphrasing Revisited with Neural Machine Translation
Recognizing and generating paraphrases is an important component in many natural language processing applications.
