“Let’s Eat Grandma”: Does Punctuation Matter in Sentence Representation?

Karami, Mansooreh; Mosallanezhad, Ahmadreza; Mancenido, Michelle V.; Liu, Huan

doi:10.1007/978-3-031-26390-3_34

Search citation statements

Order By: Relevance

Paper Sections

Select...

B Embeddings and Features1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2023

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

(1 citation statement)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Words are encoded using pre-trained GloVe embeddings [59] of size 300. Since punctuation may play a key role in the semantic of the sentences [60], we have decided to keep punctuation tokens as well. Input sequences are zero-padded to the length of the longest sequence in the datasets (henceforth T ).…”

Section: B Embeddings and Featuresmentioning

confidence: 99%

Multi-Task Attentive Residual Networks for Argument Mining

Galassi

Lippi

Torroni

2023

IEEE/ACM Trans. Audio Speech Lang. Process.

View full text Add to dashboard Cite

We explore the use of residual networks and neural attention for multiple argument mining tasks. We propose a residual architecture that exploits attention, multi-task learning, and makes use of ensemble, without any assumption on document or argument structure. We present an extensive experimental evaluation on five different corpora of user-generated comments, scientific publications, and persuasive essays. Our results show that our approach is a strong competitor against state-of-the-art architectures with a higher computational footprint or corpus-specific design, representing an interesting compromise between generality, performance accuracy and reduced model size.

show abstract

Section: B Embeddings and Featuresmentioning

confidence: 99%