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Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks
TLDR
Sentence-BERT (SBERT), a modification of the pretrained BERT network that use siamese and triplet network structures to derive semantically meaningful sentence embeddings that can be compared using cosine-similarity is presented.
A Monolingual Tree-based Translation Model for Sentence Simplification
TLDR
A Tree-based Simplification Model (TSM) is proposed, which, to the knowledge, is the first statistical simplification model covering splitting, dropping, reordering and substitution integrally.
Parsing Argumentation Structures in Persuasive Essays
TLDR
A novel approach for parsing argumentation structures is presented, which globally optimizes argument component types and argumentative relations using Integer Linear Programming and significantly outperforms challenging heuristic baselines on two different types of discourse.
UKP-Athene: Multi-Sentence Textual Entailment for Claim Verification
TLDR
This paper presents the claim verification pipeline approach, which, according to the preliminary results, scored third in the shared task, out of 23 competing systems, and introduces two extensions to the Enhanced LSTM (ESIM).
Extracting Opinion Targets in a Single and Cross-Domain Setting with Conditional Random Fields
TLDR
This paper model the problem as an information extraction task, which is addressed based on Conditional Random Fields (CRF), and employs the supervised algorithm by Zhuang et al. (2006), which represents the state-of-the-art on the employed data.
UKP: Computing Semantic Textual Similarity by Combining Multiple Content Similarity Measures
TLDR
This work uses a simple log-linear regression model, trained on the training data, to combine multiple text similarity measures of varying complexity, which range from simple character and word n-grams and common subsequences to complex features such as Explicit Semantic Analysis vector comparisons and aggregation of word similarity based on lexical-semantic resources.
MAD-X: An Adapter-based Framework for Multi-task Cross-lingual Transfer
TLDR
MAD-X is proposed, an adapter-based framework that enables high portability and parameter-efficient transfer to arbitrary tasks and languages by learning modular language and task representations and introduces a novel invertible adapter architecture and a strong baseline method for adapting a pretrained multilingual model to a new language.
Identifying Argumentative Discourse Structures in Persuasive Essays
TLDR
A novel approach for identifying argumentative discourse structures in persuasive essays by evaluating several classifiers and proposing novel feature sets including structural, lexical, syntactic and contextual features.
AdapterHub: A Framework for Adapting Transformers
TLDR
AdaptersHub is proposed, a framework that allows dynamic “stiching-in” of pre-trained adapters for different tasks and languages that enables scalable and easy access to sharing of task-specific models, particularly in low-resource scenarios.
Annotating Argument Components and Relations in Persuasive Essays
TLDR
An annotation scheme that includes the annotation of claims and premises as well as support and attack relations for capturing the structure of argumentative discourse is proposed.
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