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Found 31 papers, 20 papers with code
Diversity and Consistency: Exploring Visual Question-Answer Pair Generation
Moreover, this task can be used to improve visual question generation and visual question answering.
CLEME2.0: Towards More Interpretable Evaluation by Disentangling Edits for Grammatical Error Correction
The paper focuses on improving the interpretability of Grammatical Error Correction (GEC) metrics, which receives little attention in previous studies.
When LLMs Meet Cunning Texts: A Fallacy Understanding Benchmark for Large Language Models
In this paper, we challenge the reasoning and understanding abilities of LLMs by proposing a FaLlacy Understanding Benchmark (FLUB) containing cunning texts that are easy for humans to understand but difficult for models to grasp.
Towards Real-World Writing Assistance: A Chinese Character Checking Benchmark with Faked and Misspelled Characters
To the best of our knowledge, Visual-C$^3$ is the first real-world visual and the largest human-crafted dataset for the Chinese character checking scenario.
A Frustratingly Easy Plug-and-Play Detection-and-Reasoning Module for Chinese Spelling Check
In recent years, Chinese Spelling Check (CSC) has been greatly improved by designing task-specific pre-training methods or introducing auxiliary tasks, which mostly solve this task in an end-to-end fashion.
Enhancing Phrase Representation by Information Bottleneck Guided Text Diffusion Process for Keyphrase Extraction
Keyphrase extraction (KPE) is an important task in Natural Language Processing for many scenarios, which aims to extract keyphrases that are present in a given document.
On the (In)Effectiveness of Large Language Models for Chinese Text Correction
Recently, the development and progress of Large Language Models (LLMs) have amazed the entire Artificial Intelligence community.
Unifying Token and Span Level Supervisions for Few-Shot Sequence Labeling
However, these methods are only trained at a single granularity (i. e., either token level or span level) and have some weaknesses of the corresponding granularity.
CLEME: Debiasing Multi-reference Evaluation for Grammatical Error Correction
Evaluating the performance of Grammatical Error Correction (GEC) systems is a challenging task due to its subjectivity.
Instance Segmentation for Chinese Character Stroke Extraction, Datasets and Benchmarks
Moreover, there are no standardized benchmarks to provide a fair comparison between different stroke extraction methods, which, we believe, is a major impediment to the development of Chinese character stroke understanding and related tasks.
Linguistic Rules-Based Corpus Generation for Native Chinese Grammatical Error Correction
Extensive experiments and detailed analyses not only demonstrate that the training data constructed by our method effectively improves the performance of CGEC models, but also reflect that our benchmark is an excellent resource for further development of the CGEC field.
Learning from the Dictionary: Heterogeneous Knowledge Guided Fine-tuning for Chinese Spell Checking
Chinese Spell Checking (CSC) aims to detect and correct Chinese spelling errors.
AiM: Taking Answers in Mind to Correct Chinese Cloze Tests in Educational Applications
To automatically correct handwritten assignments, the traditional approach is to use an OCR model to recognize characters and compare them to answers.
Automatic Context Pattern Generation for Entity Set Expansion
In addition, we propose the GAPA, a novel ESE framework that leverages the aforementioned GenerAted PAtterns to expand target entities.
Contextual Similarity is More Valuable than Character Similarity: An Empirical Study for Chinese Spell Checking
Chinese Spell Checking (CSC) task aims to detect and correct Chinese spelling errors.
Type-Driven Multi-Turn Corrections for Grammatical Error Correction
First, they simply mix additionally-constructed training instances and original ones to train models, which fails to help models be explicitly aware of the procedure of gradual corrections.
The Past Mistake is the Future Wisdom: Error-driven Contrastive Probability Optimization for Chinese Spell Checking
However, there exists a gap between the learned knowledge of PLMs and the goal of CSC task.
A non-hierarchical attention network with modality dropout for textual response generation in multimodal dialogue systems
Existing text- and image-based multimodal dialogue systems use the traditional Hierarchical Recurrent Encoder-Decoder (HRED) framework, which has an utterance-level encoder to model utterance representation and a context-level encoder to model context representation.
Seeking Patterns, Not just Memorizing Procedures: Contrastive Learning for Solving Math Word Problems
Math Word Problem (MWP) solving needs to discover the quantitative relationships over natural language narratives.
An Enhanced Span-based Decomposition Method for Few-Shot Sequence Labeling
Few-Shot Sequence Labeling (FSSL) is a canonical paradigm for the tagging models, e. g., named entity recognition and slot filling, to generalize on an emerging, resource-scarce domain.
Ranked #8 on
Few-shot NER
on Few-NERD (INTER)
Read, Listen, and See: Leveraging Multimodal Information Helps Chinese Spell Checking
Chinese Spell Checking (CSC) aims to detect and correct erroneous characters for user-generated text in the Chinese language.
Ranked #2 on
Chinese Spell Checking
on SIGHAN 2015
Improving BERT with Syntax-aware Local Attention
Pre-trained Transformer-based neural language models, such as BERT, have achieved remarkable results on varieties of NLP tasks.
Dialogue Response Selection with Hierarchical Curriculum Learning
As for IC, it progressively strengthens the model's ability in identifying the mismatching information between the dialogue context and a response candidate.
Ranked #3 on
Conversational Response Selection
on RRS
Learning to Summarize Passages: Mining Passage-Summary Pairs from Wikipedia Revision Histories
In this paper, we propose a method for automatically constructing a passage-to-summary dataset by mining the Wikipedia page revision histories.
At Which Level Should We Extract? An Empirical Analysis on Extractive Document Summarization
In this work, we show that unnecessity and redundancy issues exist when extracting full sentences, and extracting sub-sentential units is a promising alternative.
Towards Generating Math Word Problems from Equations and Topics
A math word problem is a narrative with a specific topic that provides clues to the correct equation with numerical quantities and variables therein.
Neural Document Summarization by Jointly Learning to Score and Select Sentences
In this paper, we present a novel end-to-end neural network framework for extractive document summarization by jointly learning to score and select sentences.
Ranked #10 on
Extractive Text Summarization
on CNN / Daily Mail
Sequential Copying Networks
Copying mechanism shows effectiveness in sequence-to-sequence based neural network models for text generation tasks, such as abstractive sentence summarization and question generation.
Using Intermediate Representations to Solve Math Word Problems
To solve math word problems, previous statistical approaches attempt at learning a direct mapping from a problem description to its corresponding equation system.
Selective Encoding for Abstractive Sentence Summarization
We propose a selective encoding model to extend the sequence-to-sequence framework for abstractive sentence summarization.
Ranked #8 on
Text Summarization
on DUC 2004 Task 1
Neural Question Generation from Text: A Preliminary Study
Automatic question generation aims to generate questions from a text passage where the generated questions can be answered by certain sub-spans of the given passage.
Ranked #13 on
Question Generation
on SQuAD1.1
