Search Results for author:
Found 19 papers, 6 papers with code
Joint Learning-based Heterogeneous Graph Attention Network for Timeline Summarization
Previous studies on the timeline summarization (TLS) task ignored the information interaction between sentences and dates, and adopted pre-defined unlearnable representations for them.
A-TIP: Attribute-aware Text Infilling via Pre-trained Language Model
Text infilling aims to restore incomplete texts by filling in blanks, which has attracted more attention recently because of its wide application in ancient text restoration and text rewriting.
Unveiling the Power of Source: Source-based Minimum Bayes Risk Decoding for Neural Machine Translation
Maximum a posteriori decoding, a commonly used method for neural machine translation (NMT), aims to maximize the estimated posterior probability.
DiLM: Distilling Dataset into Language Model for Text-level Dataset Distillation
To address this issue, we propose a novel text dataset distillation approach, called Distilling dataset into Language Model (DiLM), which trains a language model to generate informative synthetic training samples as text data, instead of directly optimizing synthetic samples.
Joyful: Joint Modality Fusion and Graph Contrastive Learning for Multimodal Emotion Recognition
In this paper, we propose a method for joint modality fusion and graph contrastive learning for multimodal emotion recognition (Joyful), where multimodality fusion, contrastive learning, and emotion recognition are jointly optimized.
Ranked #1 on
Face Swapping
on HOD
(using extra training data)
Active Learning Based Fine-Tuning Framework for Speech Emotion Recognition
However, existing SER methods ignore the information gap between the pre-training speech recognition task and the downstream SER task, leading to sub-optimal performance.
Automatic Answerability Evaluation for Question Generation
This work proposes PMAN (Prompting-based Metric on ANswerability), a novel automatic evaluation metric to assess whether the generated questions are answerable by the reference answers for the QG tasks.
LATTE: Lattice ATTentive Encoding for Character-based Word Segmentation
Our model employs the lattice structure to handle segmentation alternatives and utilizes graph neural networks along with an attention mechanism to attentively extract multi-granularity representation from the lattice for complementing character representations.
Ranked #1 on
Chinese Word Segmentation
on CTB6
(using extra training data)
Non-Axiomatic Term Logic: A Computational Theory of Cognitive Symbolic Reasoning
This paper presents Non-Axiomatic Term Logic (NATL) as a theoretical computational framework of humanlike symbolic reasoning in artificial intelligence.
Generating Repetitions with Appropriate Repeated Words
A repetition is a response that repeats words in the previous speaker's utterance in a dialogue.
Towards Table-to-Text Generation with Numerical Reasoning
In summary, our contributions are (1) a new dataset for numerical table-to-text generation using pairs of a table and a paragraph of a table description with richer inference from scientific papers, and (2) a table-to-text generation framework enriched with numerical reasoning.
Generating Weather Comments from Meteorological Simulations
The task of generating weather-forecast comments from meteorological simulations has the following requirements: (i) the changes in numerical values for various physical quantities need to be considered, (ii) the weather comments should be dependent on delivery time and area information, and (iii) the comments should provide useful information for users.
A POS Tagging Model Adapted to Learner English
Based on the discussion of possible causes of POS tagging errors in learner English, we show that deep neural models are particularly suitable for this.
The dialogue breakdown detection challenge: Task description, datasets, and evaluation metrics
Dialogue breakdown detection is a promising technique in dialogue systems.
