157 research outputs found
Discriminative Sentence Modeling for Story Ending Prediction
Story Ending Prediction is a task that needs to select an appropriate ending
for the given story, which requires the machine to understand the story and
sometimes needs commonsense knowledge. To tackle this task, we propose a new
neural network called Diff-Net for better modeling the differences of each
ending in this task. The proposed model could discriminate two endings in three
semantic levels: contextual representation, story-aware representation, and
discriminative representation. Experimental results on the Story Cloze Test
dataset show that the proposed model siginificantly outperforms various systems
by a large margin, and detailed ablation studies are given for better
understanding our model. We also carefully examine the traditional and
BERT-based models on both SCT v1.0 and v1.5 with interesting findings that may
potentially help future studies.Comment: 8 pages, accepted as a conference paper at AAAI 202
Unsupervised Neural Stylistic Text Generation using Transfer learning and Adapters
Research has shown that personality is a key driver to improve engagement and
user experience in conversational systems. Conversational agents should also
maintain a consistent persona to have an engaging conversation with a user.
However, text generation datasets are often crowd sourced and thereby have an
averaging effect where the style of the generation model is an average style of
all the crowd workers that have contributed to the dataset. While one can
collect persona-specific datasets for each task, it would be an expensive and
time consuming annotation effort. In this work, we propose a novel transfer
learning framework which updates only of model parameters to learn
style specific attributes for response generation. For the purpose of this
study, we tackle the problem of stylistic story ending generation using the ROC
stories Corpus. We learn style specific attributes from the
PERSONALITY-CAPTIONS dataset. Through extensive experiments and evaluation
metrics we show that our novel training procedure can improve the style
generation by 200 over Encoder-Decoder baselines while maintaining on-par
content relevance metrics wit
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