Search Results for author:
Found 10 papers, 3 papers with code
Multi-Turn Target-Guided Topic Prediction with Monte Carlo Tree Search
In this paper, we formulate the target-guided conversation as a problem of multi-turn topic prediction and model it under the framework of Markov decision process (MDP).
Accurately Predicting Probabilities of Safety-Critical Rare Events for Intelligent Systems
The complexity of predicting criticality arises from the extreme data imbalance caused by rare events in high dimensional variables associated with the rare events, a challenge we refer to as the curse of rarity.
Adaptive Testing Environment Generation for Connected and Automated Vehicles with Dense Reinforcement Learning
A common approach involves designing testing scenarios based on prior knowledge of CAVs (e. g., surrogate models), conducting tests in these scenarios, and subsequently evaluating CAVs' safety performances.
Few-Shot Scenario Testing for Autonomous Vehicles Based on Neighborhood Coverage and Similarity
To alleviate the considerable uncertainty inherent in a small testing scenario set, we frame the FST problem as an optimization problem and search for the testing scenario set based on neighborhood coverage and similarity.
Adaptive Safety Evaluation for Connected and Automated Vehicles with Sparse Control Variates
One prevailing way is to design testing scenarios using prior knowledge of CAVs, test CAVs in these scenarios, and then evaluate their safety performances.
Adaptive Testing for Connected and Automated Vehicles with Sparse Control Variates in Overtaking Scenarios
To validate the proposed method, the high-dimensional overtaking scenarios are investigated, and the results demonstrate that our approach can further accelerate the evaluation process by about 30 times.
A Joint Model for Dropped Pronoun Recovery and Conversational Discourse Parsing in Chinese Conversational Speech
A second (multi-relational) GCN is then applied to the utterance states to produce a discourse relation-augmented representation for the utterances, which are then fused together with token states in each utterance as input to a dropped pronoun recovery layer.
Ranked #5 on
Discourse Parsing
on STAC
Transformer-GCRF: Recovering Chinese Dropped Pronouns with General Conditional Random Fields
Exploratory analysis also demonstrates that the GCRF did help to capture the dependencies between pronouns in neighboring utterances, thus contributes to the performance improvements.
Recovering Dropped Pronouns in Chinese Conversations via Modeling Their Referents
Pronouns are often dropped in Chinese sentences, and this happens more frequently in conversational genres as their referents can be easily understood from context.
Pre-training of Context-aware Item Representation for Next Basket Recommendation
In the offline phase, IERT pre-trains deep item representations conditioning on their transaction contexts.
