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Found 21 papers, 14 papers with code
Efficient Attribute Injection for Pretrained Language Models
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.
Aspect-Controllable Opinion Summarization
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
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
We present the Quantized Transformer (QT), an unsupervised system for extractive opinion summarization.
Retrieval-Augmented Controllable Review Generation
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.
Heads-up! Unsupervised Constituency Parsing via Self-Attention Heads
Here, we propose a novel fully unsupervised parsing approach that extracts constituency trees from PLM attention heads.
Unsupervised Opinion Summarization with Noising and Denoising
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.
Evaluating Research Novelty Detection: Counterfactual Approaches
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
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.
Informative and Controllable Opinion Summarization
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).
Rethinking Attribute Representation and Injection for Sentiment Classification
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
ThisIsCompetition at SemEval-2019 Task 9: BERT is unstable for out-of-domain samples
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.
Categorical Metadata Representation for Customized Text Classification
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)
AutoSense Model for Word Sense Induction
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
Adversarial TableQA: Attention Supervision for Question Answering on Tables
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.
Entity Commonsense Representation for Neural Abstractive Summarization
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 #19 on
Text Summarization
on GigaWord
Aspect Sentiment Model for Micro Reviews
This paper aims at an aspect sentiment model for aspect-based sentiment analysis (ABSA) focused on micro reviews.
Cold-Start Aware User and Product Attention for Sentiment Classification
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
Translations as Additional Contexts for Sentence Classification
We are the first to use translations as domain-free contexts for sentence classification.
Ranked #6 on
Text Classification
on TREC-6
Building Content-driven Entity Networks for Scarce Scientific Literature using Content Information
The results show that the co-occurrence and citation networks constructed using the proposed method outperforms the traditional-based networks.
