42 |
Open Aspect Target Sentiment Classification with Natural Language Prompts ...
|
|
|
|
BASE
|
|
Show details
|
|
43 |
Stepmothers are mean and academics are pretentious: What do pretrained language models learn about you? ...
|
|
|
|
BASE
|
|
Show details
|
|
44 |
We've had this conversation before: A Novel Approach to Measuring Dialog Similarity ...
|
|
|
|
BASE
|
|
Show details
|
|
45 |
ESTER: A Machine Reading Comprehension Dataset for Reasoning about Event Semantic Relations ...
|
|
|
|
BASE
|
|
Show details
|
|
47 |
CLIFF: Contrastive Learning for Improving Faithfulness and Factuality in Abstractive Summarization ...
|
|
|
|
BASE
|
|
Show details
|
|
49 |
Knowledge Base Completion Meets Transfer Learning ...
|
|
|
|
Abstract:
Anthology paper link: https://aclanthology.org/2021.emnlp-main.524/ Abstract: The aim of knowledge base completion is to predict unseen facts from existing facts in knowledge bases. In this work, we introduce the first approach for transfer of knowledge from one collection of facts to another without the need for entity or relation matching. The method works for both canonicalized knowledge bases and uncanonicalized or open knowledge bases, i.e., knowledge bases where more than one copy of a real-world entity or relation may exist. Such knowledge bases are a natural output of automated information extraction tools that extract structured data from unstructured text. Our main contribution is a method that can make use of a large-scale pre-training on facts, collected from unstructured text, to improve predictions on structured data from a specific domain. The introduced method is the most impactful on small datasets such as ReVerb20K, where we obtained 6% absolute increase of mean reciprocal rank and 65% ...
|
|
Keyword:
Computational Linguistics; Machine Learning; Machine Learning and Data Mining; Natural Language Processing
|
|
URL: https://underline.io/lecture/37696-knowledge-base-completion-meets-transfer-learning https://dx.doi.org/10.48448/y1fk-xn92
|
|
BASE
|
|
Hide details
|
|
50 |
Partially Supervised Named Entity Recognition via the Expected Entity Ratio Loss ...
|
|
|
|
BASE
|
|
Show details
|
|
51 |
Honey or Poison? Solving the Trigger Curse in Few-shot Event Detection via Causal Intervention ...
|
|
|
|
BASE
|
|
Show details
|
|
52 |
Analyzing the Surprising Variability in Word Embedding Stability Across Languages ...
|
|
|
|
BASE
|
|
Show details
|
|
53 |
Neural Machine Translation with Heterogeneous Topic Knowledge Embeddings ...
|
|
|
|
BASE
|
|
Show details
|
|
54 |
Towards Zero-Shot Knowledge Distillation for Natural Language Processing ...
|
|
|
|
BASE
|
|
Show details
|
|
56 |
SIMMC 2.0: A Task-oriented Dialog Dataset for Immersive Multimodal Conversations ...
|
|
|
|
BASE
|
|
Show details
|
|
57 |
Automatic Text Evaluation through the Lens of Wasserstein Barycenters ...
|
|
|
|
BASE
|
|
Show details
|
|
58 |
Combining sentence and table evidence to predict veracity of factual claims using TaPaS and RoBERTa ...
|
|
|
|
BASE
|
|
Show details
|
|
59 |
Meta Distant Transfer Learning for Pre-trained Language Models ...
|
|
|
|
BASE
|
|
Show details
|
|
|
|