Search Results for author:
Found 11 papers, 3 papers with code
Few-Shot Learning of an Interleaved Text Summarization Model by Pretraining with Synthetic Data
Interleaved texts, where posts belonging to different threads occur in a sequence, commonly occur in online chat posts, so that it can be time-consuming to quickly obtain an overview of the discussions.
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.
AAAI FSS-19: Human-Centered AI: Trustworthiness of AI Models and Data Proceedings
To facilitate the widespread acceptance of AI systems guiding decision-making in real-world applications, it is key that solutions comprise trustworthy, integrated human-AI systems.
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.
Generating Multi-Sentence Abstractive Summaries of Interleaved Texts
The interleaved posts are encoded hierarchically, i. e., word-to-word (words in a post) followed by post-to-post (posts in a channel).
A Hierarchical Decoder with Three-level Hierarchical Attention to Generate Abstractive Summaries of Interleaved Texts
Interleaved texts, where posts belonging to different threads occur in one sequence, are a common occurrence, e. g., online chat conversations.
News Article Teaser Tweets and How to Generate Them
In this work, we define the task of teaser generation and provide an evaluation benchmark and baseline systems for the process of generating teasers.
End-to-End Trainable Attentive Decoder for Hierarchical Entity Classification
We address fine-grained entity classification and propose a novel attention-based recurrent neural network (RNN) encoder-decoder that generates paths in the type hierarchy and can be trained end-to-end.
LinkedHealthAnswers: Towards Linked Data-driven Question Answering for the Health Care Domain
This paper presents Linked Health Answers, a natural language question answering systems that utilizes health data drawn from the Linked Data Cloud.
MLSA --- A Multi-layered Reference Corpus for German Sentiment Analysis
The construction of the corpus is based on the manual annotation of 270 German-language sentences considering three different layers of granularity.
