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Found 10 papers, 3 papers with code
GCOF: Self-iterative Text Generation for Copywriting Using Large Language Model
Large language models(LLM) such as ChatGPT have substantially simplified the generation of marketing copy, yet producing content satisfying domain specific requirements, such as effectively engaging customers, remains a significant challenge.
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.
Finding Similar Exercises in Retrieval Manner
When students make a mistake in an exercise, they can consolidate it by ``similar exercises'' which have the same concepts, purposes and methods.
TQ-Net: Mixed Contrastive Representation Learning For Heterogeneous Test Questions
Finally, supervised contrastive learning was conducted on relevance prediction-related downstream tasks, which helped the model to learn the representation of questions effectively.
DialogUSR: Complex Dialogue Utterance Splitting and Reformulation for Multiple Intent Detection
While interacting with chatbots, users may elicit multiple intents in a single dialogue utterance.
Exploring Student Representation For Neural Cognitive Diagnosis
In this paper, we propose a method of student representation with the exploration of the hierarchical relations of knowledge concepts and student embedding.
LANA: Towards Personalized Deep Knowledge Tracing Through Distinguishable Interactive Sequences
With pivot module reconstructed the decoder for individual students and leveled learning specialized encoders for groups, personalized DKT was achieved.
HCCL at SemEval-2018 Task 8: An End-to-End System for Sequence Labeling from Cybersecurity Reports
This paper describes HCCL team systems that participated in SemEval 2018 Task 8: SecureNLP (Semantic Extraction from cybersecurity reports using NLP).
HCCL at SemEval-2017 Task 2: Combining Multilingual Word Embeddings and Transliteration Model for Semantic Similarity
In this paper, we introduce an approach to combining word embeddings and machine translation for multilingual semantic word similarity, the task2 of SemEval-2017.
Cross-Lingual Word Embeddings
Multilingual Word Embeddings
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