1 code implementation • COLING 2022 • 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 expanding the architecture of the models to improve performance.
no code implementations • 23 May 2023 • Fantine Huot, Joshua Maynez, Chris Alberti, Reinald Kim Amplayo, Priyanka Agrawal, Constanza Fierro, Shashi Narayan, Mirella Lapata
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 across speakers of other languages.
no code implementations • 28 Apr 2023 • Fantine Huot, Joshua Maynez, Shashi Narayan, Reinald Kim Amplayo, Kuzman Ganchev, Annie Louis, Anders Sandholm, Dipanjan Das, Mirella Lapata
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 irrelevant, repetitive, and hallucinated content.
no code implementations • 31 Oct 2022 • Reinald Kim Amplayo, Kellie Webster, Michael Collins, Dipanjan Das, Shashi Narayan
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.
no code implementations • 1 Aug 2022 • Reinald Kim Amplayo, Peter J. Liu, Yao Zhao, Shashi Narayan
Specifically, We treat sentences as basic units of matching instead of tokens, and use a sentence matching function to soft-match candidate and reference sentences.
1 code implementation • 1 Jul 2022 • Shashi Narayan, Joshua Maynez, Reinald Kim Amplayo, Kuzman Ganchev, Annie Louis, Fantine Huot, Anders Sandholm, Dipanjan Das, Mirella Lapata
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 often reveal hallucinations and fail to correctly cover important details.
no code implementations • NAACL 2022 • Yu Jin Kim, Beong-woo Kwak, Youngwook Kim, Reinald Kim Amplayo, Seung-won Hwang, Jinyoung Yeo
Towards this goal, we propose to mitigate the loss of knowledge from the interference among the different knowledge sources, by developing a modular variant of the knowledge aggregation as a new zero-shot commonsense reasoning framework.
1 code implementation • 3 Jun 2022 • Reinald Kim Amplayo, Arthur Bražinskas, Yoshi Suhara, Xiaolan Wang, Bing Liu
In this tutorial, we present various aspects of opinion summarization that are useful for researchers and practitioners.
no code implementations • 16 Sep 2021 • 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 order to improve their performance.
1 code implementation • EMNLP 2021 • 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.
1 code implementation • 14 Dec 2020 • 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.
Abstractive Text Summarization
Unsupervised Opinion Summarization
2 code implementations • 8 Dec 2020 • Stefanos Angelidis, Reinald Kim Amplayo, Yoshihiko Suhara, Xiaolan Wang, Mirella Lapata
We present the Quantized Transformer (QT), an unsupervised system for extractive opinion summarization.
no code implementations • COLING 2020 • Jihyeok Kim, Seungtaek Choi, Reinald Kim Amplayo, Seung-won Hwang
We thus propose to additionally leverage references, which are selected from a large pool of texts labeled with one of the attributes, as textual information that enriches inductive biases of given attributes.
no code implementations • Asian Chapter of the Association for Computational Linguistics 2020 • Bowen Li, Taeuk Kim, Reinald Kim Amplayo, Frank Keller
Here, we propose a novel fully unsupervised parsing approach that extracts constituency trees from PLM attention heads.
1 code implementation • ACL 2020 • Reinald Kim Amplayo, Mirella Lapata
We create a synthetic dataset from a corpus of user reviews by sampling a review, pretending it is a summary, and generating noisy versions thereof which we treat as pseudo-review input.
no code implementations • WS 2019 • Reinald Kim Amplayo, Seung-won Hwang, Min Song
We find the novelty is not a singular concept, and thus inherently lacks of ground truth annotations with cross-annotator agreement, which is a major obstacle in evaluating these models.
1 code implementation • 18 Sep 2019 • Reinald Kim Amplayo, Seonjae Lim, Seung-won Hwang
We propose a state-of-the-art CLT model called Length Transfer Networks (LeTraNets) that introduces a two-way encoding scheme for short and long texts using multiple training mechanisms.
1 code implementation • EACL 2021 • 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).
1 code implementation • IJCNLP 2019 • Reinald Kim Amplayo
In this paper, we show that the above method is the least effective way to represent and inject attributes.
Ranked #2 on
Sentiment Analysis
on User and product information
no code implementations • SEMEVAL 2019 • Cheoneum Park, Juae Kim, Hyeon-gu Lee, Reinald Kim Amplayo, Harksoo Kim, Jungyun Seo, Chang-Ki Lee
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.
2 code implementations • TACL 2019 • Jihyeok Kim, Reinald Kim Amplayo, Kyungjae Lee, Sua Sung, Minji Seo, Seung-won Hwang
The performance of text classification has improved tremendously using intelligently engineered neural-based models, especially those injecting categorical metadata as additional information, e. g., using user/product information for sentiment classification.
Ranked #4 on
Sentiment Analysis
on User and product information
(Yelp 2013 (Acc) metric)
1 code implementation • 22 Nov 2018 • Reinald Kim Amplayo, Seung-won Hwang, Min Song
Thus, we aim to eliminate these requirements and solve the sense granularity problem by proposing AutoSense, a latent variable model based on two observations: (1) senses are represented as a distribution over topics, and (2) senses generate pairings between the target word and its neighboring word.
Ranked #2 on
Word Sense Induction
on SemEval 2010 WSI
no code implementations • 18 Oct 2018 • Minseok Cho, Reinald Kim Amplayo, Seung-won Hwang, Jonghyuck Park
The same question has not been asked in the table question answering (TableQA) task, where we are tasked to answer a query given a table.
1 code implementation • ACL 2018 • Reinald Kim Amplayo, Jihyeok Kim, Sua Sung, Seung-won Hwang
The use of user/product information in sentiment analysis is important, especially for cold-start users/products, whose number of reviews are very limited.
Ranked #4 on
Sentiment Analysis
on User and product information
1 code implementation • 14 Jun 2018 • Reinald Kim Amplayo, Kyungjae Lee, Jinyeong Yeo, Seung-won Hwang
We are the first to use translations as domain-free contexts for sentence classification.
Ranked #6 on
Text Classification
on TREC-6
1 code implementation • NAACL 2018 • Reinald Kim Amplayo, Seonjae Lim, Seung-won Hwang
To this end, we leverage on an off-the-shelf entity linking system (ELS) to extract linked entities and propose Entity2Topic (E2T), a module easily attachable to a sequence-to-sequence model that transforms a list of entities into a vector representation of the topic of the summary.
Ranked #22 on
Text Summarization
on GigaWord
1 code implementation • 14 Jun 2018 • Reinald Kim Amplayo, Seung-won Hwang
This paper aims at an aspect sentiment model for aspect-based sentiment analysis (ABSA) focused on micro reviews.
Aspect-Based Sentiment Analysis (ABSA)
Sentiment Classification
+2
no code implementations • WS 2016 • Reinald Kim Amplayo, Min Song
The results show that the co-occurrence and citation networks constructed using the proposed method outperforms the traditional-based networks.