5 citations · 5 across the 4 of their papers we have counts for
18 papers
Query Refinement Prompts for Closed-Book Long-Form Question Answering
Reinald Kim Amplayo, Kellie Webster, Michael Collins +2
Large language models (LLMs) have been shown to perform well in answering questions and in producing long-form texts, both in few-shot closed-book settings. While the former can be…
Efficient Attribute Injection for Pretrained Language Models
Reinald Kim Amplayo, Kang Min Yoo, Sang-Woo Lee
Metadata attributes (e.g., user and product IDs from reviews) can be incorporated as additional inputs to neural-based NLP models, by modifying the architecture of the models, in o…
Aspect-Controllable Opinion Summarization
Reinald Kim Amplayo, Stefanos Angelidis, Mirella Lapata
Recent work on opinion summarization produces general summaries based on a set of input reviews and the popularity of opinions expressed in them. In this paper, we propose an appro…
Unsupervised Opinion Summarization with Content Planning
Reinald Kim Amplayo, Stefanos Angelidis, Mirella Lapata
The recent success of deep learning techniques for abstractive summarization is predicated on the availability of large-scale datasets. When summarizing reviews (e.g., for products…
Extractive Opinion Summarization in Quantized Transformer Spaces
Stefanos Angelidis, Reinald Kim Amplayo, Yoshihiko Suhara +2
We present the Quantized Transformer (QT), an unsupervised system for extractive opinion summarization. QT is inspired by Vector-Quantized Variational Autoencoders, which we repurp…
Heads-up! Unsupervised Constituency Parsing via Self-Attention Heads
Bowen Li, Taeuk Kim, Reinald Kim Amplayo +1
Transformer-based pre-trained language models (PLMs) have dramatically improved the state of the art in NLP across many tasks. This has led to substantial interest in analyzing the…