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Benchmarking Answer Verification Methods for Question Answering-Based Summarization Evaluation Metrics ...
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Question-Based Salient Span Selection for More Controllable Text Summarization ...
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Did Aristotle Use a Laptop? A Question Answering Benchmark with Implicit Reasoning Strategies ...
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ESTER: A Machine Reading Comprehension Dataset for Reasoning about Event Semantic Relations ...
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What is Your Article Based On? Inferring Fine-grained Provenance ...
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BabyBERTa: Learning More Grammar With Small-Scale Child-Directed Language ...
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{Z}ero-shot {L}abel-Aware {E}vent {T}rigger and {A}rgument {C}lassification ...
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Zero-shot Event Extraction via Transfer Learning: Challenges and Insights ...
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Do We Know What We Don't Know? Studying Unanswerable Questions beyond SQuAD 2.0 ...
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Towards Question-Answering as an Automatic Metric for Evaluating the Content Quality of a Summary ...
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Constrained Labeled Data Generation for Low-Resource Named Entity Recognition ...
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Cross-lingual Entity Alignment with Incidental Supervision ...
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Abstract:
Much research effort has been put to multilingual knowledge graph (KG) embedding methods to address the entity alignment task, which seeks to match entities in different languagespecific KGs that refer to the same real-world object. Such methods are often hindered by the insufficiency of seed alignment provided between KGs. Therefore, we propose an incidentally supervised model, JEANS , which jointly represents multilingual KGs and text corpora in a shared embedding scheme, and seeks to improve entity alignment with incidental supervision signals from text. JEANS first deploys an entity grounding process to combine each KG with the monolingual text corpus. Then, two learning processes are conducted: (i) an embedding learning process to encode the KG and text of each language in one embedding space, and (ii) a selflearning based alignment learning process to iteratively induce the matching of entities and that of lexemes between embeddings. Experiments on benchmark datasets show that JEANS leads to promising ... : EACL 2021 ...
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Keyword:
Artificial Intelligence cs.AI; Computation and Language cs.CL; FOS Computer and information sciences; Information Retrieval cs.IR
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URL: https://arxiv.org/abs/2005.00171 https://dx.doi.org/10.48550/arxiv.2005.00171
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TransOMCS: From Linguistic Graphs to Commonsense Knowledge ...
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Is Killed More Significant than Fled? A Contextual Model for Salient Event Detection ...
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Towards Question-Answering as an Automatic Metric for Evaluating the Content Quality of a Summary ...
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Extending Wikification: Nominal discovery, nominal linking, and the grounding of nouns
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