Search Results for author:
Found 31 papers, 17 papers with code
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.
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 #7 on
Text Classification
on TREC-6
Aspect Sentiment Model for Micro Reviews
This paper aims at an aspect sentiment model for aspect-based sentiment analysis (ABSA) focused on micro reviews.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+3
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 #23 on
Text Summarization
on GigaWord
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.
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
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)
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.
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
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).
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.
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.
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.
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.
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.
Extractive Opinion Summarization in Quantized Transformer Spaces
We present the Quantized Transformer (QT), an unsupervised system for extractive opinion summarization.
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.
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.
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.
Beyond Opinion Mining: Summarizing Opinions of Customer Reviews
In this tutorial, we present various aspects of opinion summarization that are useful for researchers and practitioners.
Modularized Transfer Learning with Multiple Knowledge Graphs for Zero-shot Commonsense Reasoning
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.
Conditional Generation with a Question-Answering Blueprint
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.
SMART: Sentences as Basic Units for Text Evaluation
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.
Query Refinement Prompts for Closed-Book Long-Form Question Answering
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.
Text-Blueprint: An Interactive Platform for Plan-based Conditional Generation
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.
$μ$PLAN: Summarizing using a Content Plan as Cross-Lingual Bridge
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.
Gemini: A Family of Highly Capable Multimodal Models
This report introduces a new family of multimodal models, Gemini, that exhibit remarkable capabilities across image, audio, video, and text understanding.
Ranked #1 on
Multi-task Language Understanding
on MMLU
(using extra training data)
Learning to Plan and Generate Text with Citations
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 supporting evidence.
Attribute Injection for Pretrained Language Models: A New Benchmark and an Efficient Method
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.
