138 research outputs found
Commonsense knowledge enhanced memory network for stance classification
Stance classification aims at identifying, in the text, the attitude toward the given targets as favorable, negative, or unrelated. In existing models for stance classification, only textual representation is leveraged, while commonsense knowledge is ignored. In order to better incorporate commonsense knowledge into stance classification, we propose a novel model named commonsense knowledge enhanced memory network, which jointly represents textual and commonsense knowledge representation of given target and text. The textual memory module in our model treats the textual representation as memory vectors, and uses attention mechanism to embody the important parts. For commonsense knowledge memory module, we jointly leverage the entity and relation embeddings learned by TransE model to take full advantage of constraints of the knowledge graph. Experimental results on the SemEval dataset show that the combination of the commonsense knowledge memory and textual memory can improve stance classification
Targeted aspect based multimodal sentiment analysis:an attention capsule extraction and multi-head fusion network
Multimodal sentiment analysis has currently identified its significance in a
variety of domains. For the purpose of sentiment analysis, different aspects of
distinguishing modalities, which correspond to one target, are processed and
analyzed. In this work, we propose the targeted aspect-based multimodal
sentiment analysis (TABMSA) for the first time. Furthermore, an attention
capsule extraction and multi-head fusion network (EF-Net) on the task of TABMSA
is devised. The multi-head attention (MHA) based network and the ResNet-152 are
employed to deal with texts and images, respectively. The integration of MHA
and capsule network aims to capture the interaction among the multimodal
inputs. In addition to the targeted aspect, the information from the context
and the image is also incorporated for sentiment delivered. We evaluate the
proposed model on two manually annotated datasets. the experimental results
demonstrate the effectiveness of our proposed model for this new task
Exploiting BERT for End-to-End Aspect-based Sentiment Analysis
In this paper, we investigate the modeling power of contextualized embeddings
from pre-trained language models, e.g. BERT, on the E2E-ABSA task.
Specifically, we build a series of simple yet insightful neural baselines to
deal with E2E-ABSA. The experimental results show that even with a simple
linear classification layer, our BERT-based architecture can outperform
state-of-the-art works. Besides, we also standardize the comparative study by
consistently utilizing a hold-out validation dataset for model selection, which
is largely ignored by previous works. Therefore, our work can serve as a
BERT-based benchmark for E2E-ABSA.Comment: NUT workshop@EMNLP-IJCNLP-201
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