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Found 31 papers, 9 papers with code
IRAC: A Domain-Specific Annotated Corpus of Implicit Reasoning in Arguments
To solve this problem, we hypothesize that as human reasoning is guided by innate collection of domain-specific knowledge, it might be beneficial to create such a domain-specific corpus for machines.
Learning and Evaluating Character Representations in Novels
We address the problem of learning fixed-length vector representations of characters in novels.
Exploring Methodologies for Collecting High-Quality Implicit Reasoning in Arguments
Annotation of implicit reasoning (i. e., warrant) in arguments is a critical resource to train models in gaining deeper understanding and correct interpretation of arguments.
LPAttack: A Feasible Annotation Scheme for Capturing Logic Pattern of Attacks in Arguments
Attacking is not always straightforward and often comprise complex rhetorical moves such that arguers might agree with a logic of an argument while attacking another logic.
TYPIC: A Corpus of Template-Based Diagnostic Comments on Argumentation
In this paper, we define three criteria that a template set should satisfy: expressiveness, informativeness, and uniqueness, and verify the feasibility of creating a template set that satisfies these criteria as a first trial.
Annotating Implicit Reasoning in Arguments with Causal Links
Most of the existing work that focus on the identification of implicit knowledge in arguments generally represent implicit knowledge in the form of commonsense or factual knowledge.
Cleaning Dirty Books: Post-OCR Processing for Previously Scanned Texts
We present methods to handle these errors, evaluated on a collection of 19, 347 texts from the Project Gutenberg dataset and 96, 635 texts from the HathiTrust Library.
Summarize-then-Answer: Generating Concise Explanations for Multi-hop Reading Comprehension
Instead, we advocate for an abstractive approach, where we propose to generate a question-focused, abstractive summary of input paragraphs and then feed it to an RC system.
A Comparative Study on Collecting High-Quality Implicit Reasonings at a Large-scale
We construct a preliminary dataset of 6, 000 warrants annotated over 600 arguments for 3 debatable topics.
Two Training Strategies for Improving Relation Extraction over Universal Graph
This paper explores how the Distantly Supervised Relation Extraction (DS-RE) can benefit from the use of a Universal Graph (UG), the combination of a Knowledge Graph (KG) and a large-scale text collection.
Modeling Event Salience in Narratives via Barthes' Cardinal Functions
Events in a narrative differ in salience: some are more important to the story than others.
Corruption Is Not All Bad: Incorporating Discourse Structure into Pre-training via Corruption for Essay Scoring
Existing approaches for automated essay scoring and document representation learning typically rely on discourse parsers to incorporate discourse structure into text representation.
Improving Evidence Detection by Leveraging Warrants
Recognizing the implicit link between a claim and a piece of evidence (i. e. warrant) is the key to improving the performance of evidence detection.
When Choosing Plausible Alternatives, Clever Hans can be Clever
Pretrained language models, such as BERT and RoBERTa, have shown large improvements in the commonsense reasoning benchmark COPA.
Inject Rubrics into Short Answer Grading System
Most existing SAG systems predict scores based only on the answers, including the model used as base line in this paper, which gives the-state-of-the-art performance.
R4C: A Benchmark for Evaluating RC Systems to Get the Right Answer for the Right Reason
Recent studies have revealed that reading comprehension (RC) systems learn to exploit annotation artifacts and other biases in current datasets.
Riposte! A Large Corpus of Counter-Arguments
Counter-arguments (CAs), one form of constructive feedback, have been proven to be useful for critical thinking skills.
Unsupervised Learning of Discourse-Aware Text Representation for Essay Scoring
Existing document embedding approaches mainly focus on capturing sequences of words in documents.
An Empirical Study of Span Representations in Argumentation Structure Parsing
For several natural language processing (NLP) tasks, span representation design is attracting considerable attention as a promising new technique; a common basis for an effective design has been established.
Distantly Supervised Biomedical Knowledge Acquisition via Knowledge Graph Based Attention
In this work, we firstly investigate the feasibility of this framework on scientific dataset, specifically on biomedical dataset.
Annotating with Pros and Cons of Technologies in Computer Science Papers
Specifically, we show how to adapt the targeted sentiment analysis task for pros/cons extraction in computer science papers and conduct an annotation study.
Feasible Annotation Scheme for Capturing Policy Argument Reasoning using Argument Templates
Most of the existing works on argument mining cast the problem of argumentative structure identification as classification tasks (e. g. attack-support relations, stance, explicit premise/claim).
A Corpus of Deep Argumentative Structures as an Explanation to Argumentative Relations
Our coverage result of 74. 6% indicates that argumentative relations can reasonably be explained by our small pattern set.
Generating Stylistically Consistent Dialog Responses with Transfer Learning
We propose a novel, data-driven, and stylistically consistent dialog response generation system.
An RNN-based Binary Classifier for the Story Cloze Test
In this paper we present a system that performs this task using a supervised binary classifier on top of a recurrent neural network to predict the probability that a given story ending is correct.
Modeling Context-sensitive Selectional Preference with Distributed Representations
This paper proposes a novel problem setting of selectional preference (SP) between a predicate and its arguments, called as context-sensitive SP (CSP).
