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Found 12 papers, 6 papers with code
Data Centric Domain Adaptation for Historical Text with OCR Errors
We propose new methods for in-domain and cross-domain Named Entity Recognition (NER) on historical data for Dutch and French.
Inexpensive Domain Adaptation of Pretrained Language Models: Case Studies on Biomedical NER and Covid-19 QA
Domain adaptation of Pretrained Language Models (PTLMs) is typically achieved by unsupervised pretraining on target-domain text.
E-BERT: Efficient-Yet-Effective Entity Embeddings for BERT
We present a novel way of injecting factual knowledge about entities into the pretrained BERT model (Devlin et al., 2019): We align Wikipedia2Vec entity vectors (Yamada et al., 2016) with BERT's native wordpiece vector space and use the aligned entity vectors as if they were wordpiece vectors.
Sentence Meta-Embeddings for Unsupervised Semantic Textual Similarity
We address the task of unsupervised Semantic Textual Similarity (STS) by ensembling diverse pre-trained sentence encoders into sentence meta-embeddings.
Multi-View Domain Adapted Sentence Embeddings for Low-Resource Unsupervised Duplicate Question Detection
We address the problem of Duplicate Question Detection (DQD) in low-resource domain-specific Community Question Answering forums.
Interpretable Question Answering on Knowledge Bases and Text
Our results suggest that IP provides better explanations than LIME or attention, according to both automatic and human evaluation.
Aligning Very Small Parallel Corpora Using Cross-Lingual Word Embeddings and a Monogamy Objective
Count-based word alignment methods, such as the IBM models or fast-align, struggle on very small parallel corpora.
Interpretable Textual Neuron Representations for NLP
Input optimization methods, such as Google Deep Dream, create interpretable representations of neurons for computer vision DNNs.
Evaluating neural network explanation methods using hybrid documents and morphosyntactic agreement
The behavior of deep neural networks (DNNs) is hard to understand.
Neural Architectures for Open-Type Relation Argument Extraction
In this work, we introduce the task of Open-Type Relation Argument Extraction (ORAE): Given a corpus, a query entity Q and a knowledge base relation (e. g.,"Q authored notable work with title X"), the model has to extract an argument of non-standard entity type (entities that cannot be extracted by a standard named entity tagger, e. g. X: the title of a book or a work of art) from the corpus.
Evaluating neural network explanation methods using hybrid documents and morphological agreement
The behavior of deep neural networks (DNNs) is hard to understand.
