Instruct: Given the title of a paper, retrieve paper titles that are cited by the given paper.
Query: The Fourth PASCAL Recognizing Textual Entailment Challenge
The third PASCAL recognizing textual entailment challenge
Recognizing Textual Entailment via Multi-task Knowledge Assisted LSTM
AdvEntuRe: Adversarial Training for Textual Entailment with Knowledge-Guided Examples
End-Task Oriented Textual Entailment via Deep Explorations of Inter-Sentence Interactions
Refining Raw Sentence Representations for Textual Entailment Recognition via Attention
Max-Cosine Matching Based Neural Models for Recognizing Textual Entailment
Tree Edit Models for Recognizing Textual Entailments, Paraphrases, and Answers to Questions
A Logic-based Approach for Recognizing Textual Entailment Supported by Ontological Background Knowledge
Learning Textual Entailment Classification from a Chinese RITE Dataset Specialized for Linguistic Phenomena
Zero-shot Relation Classification as Textual Entailment
Recognizing and Justifying Text Entailment through Distributional Navigation on Definition Graphs
Exploring Lexical, Syntactic, and Semantic Features for Chinese Textual Entailment in NTCIR RITE Evaluation Tasks
Recognising textual entailment with robust logical inference
A Survey of Paraphrasing and Textual Entailment Methods
ECNU: One Stone Two Birds: Ensemble of Heterogenous Measures for Semantic Relatedness and Textual Entailment
Recognizing Textual Entailment Using Sentence Similarity based on Dependency Tree Skeletons
CSReader at SemEval-2018 Task 11: Multiple Choice Question Answering as Textual Entailment
Reading and Thinking: Re-read LSTM Unit for Textual Entailment Recognition
A Context-Enriched Neural Network Method for Recognizing Lexical Entailment
Recognizing textual entailment with lcc’s groundhog system
Audio event detection from acoustic unit occurrence patterns
0.6 V Voltage Shifter and Clocked Comparator for Sampling Correlation-Based Impulse Radio UWB Receiver
Sliding-window filtering: an efficient algorithm for incremental mining
PROBABILISTIC TEXTUAL ENTAILMENT: GENERIC APPLIED MODELING OF LANGUAGE VARIABILITY
A constraint-based method for solving sequential manipulation planning problems