Search Results for author: Reinald Kim Amplayo

Found 21 papers, 14 papers with code

Efficient Attribute Injection for Pretrained Language Models

no code implementations16 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.

Pretrained Language Models

Aspect-Controllable Opinion Summarization

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.

Unsupervised Opinion Summarization with Content Planning

1 code implementation14 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

Extractive Opinion Summarization in Quantized Transformer Spaces

2 code implementations8 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.

Extract Aspect

Retrieval-Augmented Controllable Review Generation

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.

Review Generation Text Generation

Unsupervised Opinion Summarization with Noising and Denoising

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.

Abstractive Text Summarization Denoising +1

Evaluating Research Novelty Detection: Counterfactual Approaches

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.

Text Length Adaptation in Sentiment Classification

1 code implementation18 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.

Classification General Classification +2

Informative and Controllable Opinion Summarization

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).

ThisIsCompetition at SemEval-2019 Task 9: BERT is unstable for out-of-domain samples

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.

Suggestion mining Translation +1

Categorical Metadata Representation for Customized Text Classification

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)

Classification General Classification +3

AutoSense Model for Word Sense Induction

1 code implementation22 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.

Word Sense Induction

Adversarial TableQA: Attention Supervision for Question Answering on Tables

no code implementations18 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.

Question Answering

Entity Commonsense Representation for Neural Abstractive Summarization

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.

Abstractive Text Summarization Entity Linking

Aspect Sentiment Model for Micro Reviews

1 code implementation14 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 Term Extraction +1

Cold-Start Aware User and Product Attention for Sentiment Classification

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.

Classification General Classification +1

Building Content-driven Entity Networks for Scarce Scientific Literature using Content Information

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.

Entity Extraction using GAN

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