Papers with DPPs

5 papers
Improving the Similarity Measure of Determinantal Point Processes for Extractive Multi-Document Summarization (P19-1)

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Challenge: Despite the empirical success of multi-document summarization, most datasets remain small and the cost of hiring hu-1 is prohibitive.
Approach: They propose a novel method for extractive multi-document summarization that measures redundancy between a pair of sentences based on surface form and semantic information.
Outcome: The proposed method outperforms baseline methods on benchmark datasets and is particularly useful for documents created by multiple authors containing redundant yet lexically diverse expressions.
Diversity-Aware Batch Active Learning for Dependency Parsing (2021.naacl-main)

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Challenge: a high annotation cost for dependency parsers is a challenge . batch active learning (AL) is based on batch mode, which is more efficient for annotators to label in bulk.
Approach: They propose to reduce the number of labeled examples needed to train a strong dependency parser using batch active learning.
Outcome: The proposed approach improves on an English newswire corpus by enforcing diversity in the sampled batches.
DPP-TTS: Diversifying prosodic features of speech via determinantal point processes (2023.emnlp-main)

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Challenge: Recent advances in deep generative models have succeeded in synthesizing human-like speech.
Approach: They propose a text-to-speech model with a prosody diversifying module that considers perceptual diversity in each sample and among multiple samples.
Outcome: The proposed model generates speech samples with more diversified prosody than baselines in the side-by-side comparison test considering the naturalness of speech at the same time.
Diverse and Non-redundant Answer Set Extraction on Community QA based on DPPs (2020.coling-main)

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Challenge: Community-based question answering platforms take time to get useful information from among many answers.
Approach: They propose a method to select a diverse and non-redundant answer set rather than ranking the answers.
Outcome: The proposed method outperforms baseline methods on a Japanese CQA site . it calculates the answer importance and similarity between answers by using BERT .
Determinantal Beam Search (2021.acl-long)

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Challenge: a new beam search approach allows for a diverse subset selection process . standard beam search does not encode interactions between candidates .
Approach: They propose a beam search reformulation that casts subset selection as the subdeterminant optimization problem.
Outcome: The proposed method offers competitive performance against other diverse set generation strategies while providing a more general approach to optimizing for diversity.

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