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Found 13 papers, 4 papers with code
Grammatical Error Correction with Contrastive Learning in Low Error Density Domains
Although grammatical error correction (GEC) has achieved good performance on texts written by learners of English as a second language, performance on low error density domains where texts are written by English speakers of varying levels of proficiency can still be improved.
MedDialog: Large-scale Medical Dialogue Datasets
We also study the transferability of models trained on MedDialog to low-resource medical dialogue generation tasks.
A Scope Sensitive and Result Attentive Model for Multi-Intent Spoken Language Understanding
Result Attention Network effectively utilizes the bidirectional interaction between results of slot filling and intent detection, mitigating the error propagation problem.
Capture Salient Historical Information: A Fast and Accurate Non-Autoregressive Model for Multi-turn Spoken Language Understanding
SHA captures salient historical information for the current dialogue from both historical utterances and results via a well-designed history-attention mechanism.
An Effective Non-Autoregressive Model for Spoken Language Understanding
Spoken Language Understanding (SLU), a core component of the task-oriented dialogue system, expects a shorter inference latency due to the impatience of humans.
On the Generation of Medical Dialogs for COVID-19
Training complex dialog generation models on small datasets bears high risk of overfitting.
A Result based Portable Framework for Spoken Language Understanding
Spoken language understanding (SLU), which is a core component of the task-oriented dialogue system, has made substantial progress in the research of single-turn dialogue.
On the Generation of Medical Dialogues for COVID-19
On these two datasets, we train several dialogue generation models based on Transformer, GPT, and BERT-GPT.
Interactive Variance Attention based Online Spoiler Detection for Time-Sync Comments
In this paper, we proposed a novel Similarity-Based Network with Interactive Variance Attention (SBN-IVA) to classify comments as spoilers or not.
Improving Abstractive Document Summarization with Salient Information Modeling
Comprehensive document encoding and salient information selection are two major difficulties for generating summaries with adequate salient information.
Legal Judgment Prediction via Multi-Perspective Bi-Feedback Network
To distinguish cases with similar descriptions but different penalties, we integrate word collocations features of fact descriptions into the network via an attention mechanism.
Time-sync Video Tag Extraction Using Semantic Association Graph
Finally, we gain the weight of each word by combining Semantic Weight (SW) and Inverse Document Frequency (IDF).
Herding Effect based Attention for Personalized Time-Sync Video Recommendation
Time-sync comment (TSC) is a new form of user-interaction review associated with real-time video contents, which contains a user's preferences for videos and therefore well suited as the data source for video recommendations.
