16 citations · 57 across the 24 of their papers we have counts for
29 papers
Fabula: Building a Narrative Storytelling Sidekick with the Writers' Community
Piotr Mirowski, Ben Wedin, Reinald Kim Amplayo +14
We design and evaluate Fabula, an interactive app for fiction writers. Fabula uses detailed narrative plans informed by general narratological theory. Stories are structured hierar…
Seed-Free Synthetic Data Generation Framework for Instruction-Tuning LLMs: A Case Study in Thai
Parinthapat Pengpun, Can Udomcharoenchaikit, Weerayut Buaphet +1
We present a synthetic data approach for instruction-tuning large language models (LLMs) for low-resource languages in a data-efficient manner, specifically focusing on Thai. We id…
Agents' Room: Narrative Generation through Multi-step Collaboration
Fantine Huot, Reinald Kim Amplayo, Jennimaria Palomaki +3
Writing compelling fiction is a multifaceted process combining elements such as crafting a plot, developing interesting characters, and using evocative language. While large langua…
Scalable and Domain-General Abstractive Proposition Segmentation
Mohammad Javad Hosseini, Yang Gao, Tim Baumgärtner +2
Segmenting text into fine-grained units of meaning is important to a wide range of NLP applications. The default approach of segmenting text into sentences is often insufficient, e…
Learning to Plan and Generate Text with Citations
Constanza Fierro, Reinald Kim Amplayo, Fantine Huot +4
The increasing demand for the deployment of LLMs in information-seeking scenarios has spurred efforts in creating verifiable systems, which generate responses to queries along with…
PLAN: Summarizing using a Content Plan as Cross-Lingual Bridge
Fantine Huot, Joshua Maynez, Chris Alberti +5
Cross-lingual summarization consists of generating a summary in one language given an input document in a different language, allowing for the dissemination of relevant content acr…
Text-Blueprint: An Interactive Platform for Plan-based Conditional Generation
Fantine Huot, Joshua Maynez, Shashi Narayan +6
While conditional generation models can now generate natural language well enough to create fluent text, it is still difficult to control the generation process, leading to irrelev…
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…
SMART: Sentences as Basic Units for Text Evaluation
Reinald Kim Amplayo, Peter J. Liu, Yao Zhao +1
Widely used evaluation metrics for text generation either do not work well with longer texts or fail to evaluate all aspects of text quality. In this paper, we introduce a new metr…
Conditional Generation with a Question-Answering Blueprint
Shashi Narayan, Joshua Maynez, Reinald Kim Amplayo +6
The ability to convey relevant and faithful information is critical for many tasks in conditional generation and yet remains elusive for neural seq-to-seq models whose outputs ofte…
Modularized Transfer Learning with Multiple Knowledge Graphs for Zero-shot Commonsense Reasoning
Yu Jin Kim, Beong-woo Kwak, Youngwook Kim +3
Commonsense reasoning systems should be able to generalize to diverse reasoning cases. However, most state-of-the-art approaches depend on expensive data annotations and overfit to…
Beyond Opinion Mining: Summarizing Opinions of Customer Reviews
Reinald Kim Amplayo, Arthur Bražinskas, Yoshi Suhara +2
Customer reviews are vital for making purchasing decisions in the Information Age. Such reviews can be automatically summarized to provide the user with an overview of opinions. In…
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…
Unsupervised Opinion Summarization with Noising and Denoising
Reinald Kim Amplayo, Mirella Lapata
The supervised training of high-capacity models on large datasets containing hundreds of thousands of document-summary pairs is critical to the recent success of deep learning tech…
Text Length Adaptation in Sentiment Classification
Reinald Kim Amplayo, Seonjae Lim, Seung-won Hwang
Can a text classifier generalize well for datasets where the text length is different? For example, when short reviews are sentiment-labeled, can these transfer to predict the sent…
Informative and Controllable Opinion Summarization
Reinald Kim Amplayo, Mirella Lapata
Opinion summarization is the task of automatically generating summaries for a set of reviews about a specific target (e.g., a movie or a product). Since the number of reviews for e…
Rethinking Attribute Representation and Injection for Sentiment Classification
Reinald Kim Amplayo
Text attributes, such as user and product information in product reviews, have been used to improve the performance of sentiment classification models. The de facto standard method…
ThisIsCompetition at SemEval-2019 Task 9: BERT is unstable for out-of-domain samples
Cheoneum Park, Juae Kim, Hyeon-gu Lee +4
This paper describes our system, Joint Encoders for Stable Suggestion Inference (JESSI), for the SemEval 2019 Task 9: Suggestion Mining from Online Reviews and Forums. JESSI is a c…
Categorical Metadata Representation for Customized Text Classification
Jihyeok Kim, Reinald Kim Amplayo, Kyungjae Lee +3
The performance of text classification has improved tremendously using intelligently engineered neural-based models, especially those injecting categorical metadata as additional i…
AutoSense Model for Word Sense Induction
Reinald Kim Amplayo, Seung-won Hwang, Min Song
Word sense induction (WSI), or the task of automatically discovering multiple senses or meanings of a word, has three main challenges: domain adaptability, novel sense detection, a…