1,294 research outputs found
OATS: Opinion Aspect Target Sentiment Quadruple Extraction Dataset for Aspect-Based Sentiment Analysis
Aspect-based sentiment Analysis (ABSA) delves into understanding sentiments
specific to distinct elements within textual content. It aims to analyze
user-generated reviews to determine a) the target entity being reviewed, b) the
high-level aspect to which it belongs, c) the sentiment words used to express
the opinion, and d) the sentiment expressed toward the targets and the aspects.
While various benchmark datasets have fostered advancements in ABSA, they often
come with domain limitations and data granularity challenges. Addressing these,
we introduce the OATS dataset, which encompasses three fresh domains and
consists of 20,000 sentence-level quadruples and 13,000 review-level tuples.
Our initiative seeks to bridge specific observed gaps: the recurrent focus on
familiar domains like restaurants and laptops, limited data for intricate
quadruple extraction tasks, and an occasional oversight of the synergy between
sentence and review-level sentiments. Moreover, to elucidate OATS's potential
and shed light on various ABSA subtasks that OATS can solve, we conducted
in-domain and cross-domain experiments, establishing initial baselines. We hope
the OATS dataset augments current resources, paving the way for an encompassing
exploration of ABSA.Comment: Initial submissio
Lessons Learned from EVALITA 2020 and Thirteen Years of Evaluation of Italian Language Technology
This paper provides a summary of the 7th Evaluation Campaign of Natural Language Processing and Speech Tools for Italian (EVALITA2020) which was held online on December 17th, due to the 2020 COVID-19 pandemic. The 2020 edition of Evalita included 14 different tasks belonging to five research areas, namely: (i) Affect, Hate, and Stance, (ii) Creativity and Style, (iii) New Challenges in Long-standing Tasks, (iv) Semantics and Multimodality, (v) Time and Diachrony. This paper provides a description of the tasks and the key findings from the analysis of participant outcomes. Moreover, it provides a detailed analysis of the participants and task organizers which demonstrates the growing interest with respect to this campaign. Finally, a detailed analysis of the evaluation of tasks across the past seven editions is provided; this allows to assess how the research carried out by the Italian community dealing with Computational Linguistics has evolved in terms of popular tasks and paradigms during the last 13 years
A Novel Energy based Model Mechanism for Multi-modal Aspect-Based Sentiment Analysis
Multi-modal aspect-based sentiment analysis (MABSA) has recently attracted
increasing attention. The span-based extraction methods, such as FSUIE,
demonstrate strong performance in sentiment analysis due to their joint
modeling of input sequences and target labels. However, previous methods still
have certain limitations: (i) They ignore the difference in the focus of visual
information between different analysis targets (aspect or sentiment). (ii)
Combining features from uni-modal encoders directly may not be sufficient to
eliminate the modal gap and can cause difficulties in capturing the image-text
pairwise relevance. (iii) Existing span-based methods for MABSA ignore the
pairwise relevance of target span boundaries. To tackle these limitations, we
propose a novel framework called DQPSA for multi-modal sentiment analysis.
Specifically, our model contains a Prompt as Dual Query (PDQ) module that uses
the prompt as both a visual query and a language query to extract prompt-aware
visual information and strengthen the pairwise relevance between visual
information and the analysis target. Additionally, we introduce an Energy-based
Pairwise Expert (EPE) module that models the boundaries pairing of the analysis
target from the perspective of an Energy-based Model. This expert predicts
aspect or sentiment span based on pairwise stability. Experiments on three
widely used benchmarks demonstrate that DQPSA outperforms previous approaches
and achieves a new state-of-the-art performance.Comment: AAAI202
State-of-the-art generalisation research in NLP: a taxonomy and review
The ability to generalise well is one of the primary desiderata of natural
language processing (NLP). Yet, what `good generalisation' entails and how it
should be evaluated is not well understood, nor are there any common standards
to evaluate it. In this paper, we aim to lay the ground-work to improve both of
these issues. We present a taxonomy for characterising and understanding
generalisation research in NLP, we use that taxonomy to present a comprehensive
map of published generalisation studies, and we make recommendations for which
areas might deserve attention in the future. Our taxonomy is based on an
extensive literature review of generalisation research, and contains five axes
along which studies can differ: their main motivation, the type of
generalisation they aim to solve, the type of data shift they consider, the
source by which this data shift is obtained, and the locus of the shift within
the modelling pipeline. We use our taxonomy to classify over 400 previous
papers that test generalisation, for a total of more than 600 individual
experiments. Considering the results of this review, we present an in-depth
analysis of the current state of generalisation research in NLP, and make
recommendations for the future. Along with this paper, we release a webpage
where the results of our review can be dynamically explored, and which we
intend to up-date as new NLP generalisation studies are published. With this
work, we aim to make steps towards making state-of-the-art generalisation
testing the new status quo in NLP.Comment: 35 pages of content + 53 pages of reference
Dual Encoder: Exploiting the Potential of Syntactic and Semantic for Aspect Sentiment Triplet Extraction
Aspect Sentiment Triple Extraction (ASTE) is an emerging task in fine-grained
sentiment analysis. Recent studies have employed Graph Neural Networks (GNN) to
model the syntax-semantic relationships inherent in triplet elements. However,
they have yet to fully tap into the vast potential of syntactic and semantic
information within the ASTE task. In this work, we propose a \emph{Dual
Encoder: Exploiting the potential of Syntactic and Semantic} model (D2E2S),
which maximizes the syntactic and semantic relationships among words.
Specifically, our model utilizes a dual-channel encoder with a BERT channel to
capture semantic information, and an enhanced LSTM channel for comprehensive
syntactic information capture. Subsequently, we introduce the heterogeneous
feature interaction module to capture intricate interactions between dependency
syntax and attention semantics, and to dynamically select vital nodes. We
leverage the synergy of these modules to harness the significant potential of
syntactic and semantic information in ASTE tasks. Testing on public benchmarks,
our D2E2S model surpasses the current state-of-the-art(SOTA), demonstrating its
effectiveness.Comment: Accepted by COLING 202
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