scholarly journals Recognizing textual entailment: Rational, evaluation and approaches – Erratum

2010 ◽  
Vol 16 (1) ◽  
pp. 105-105 ◽  
Author(s):  
IDO DAGAN ◽  
BILL DOLAN ◽  
BERNARDO MAGNINI ◽  
DAN ROTH
Keyword(s):  
Natural Language ◽  
Language Engineering ◽  
Textual Entailment ◽  
Recognizing Textual Entailment ◽  
Rational Evaluation

Due to publisher error, this article was omitted from the printed issue of Natural Language Engineering volume 15 issue 4.It is published online in the correct volume (journals.cambridge.org/nle) and also printed here in volume 16 issue 1. Sincere apologies are extended to the authors for this error.

scholarly journals Combining Axiom Injection and Knowledge Base Completion for Efficient Natural Language Inference

2019 ◽  
Vol 33 ◽  
pp. 7410-7417 ◽  
Author(s):  
Masashi Yoshikawa ◽  
Koji Mineshima ◽  
Hiroshi Noji ◽  
Daisuke Bekki
Keyword(s):  
Natural Language ◽  
Knowledge Base ◽  
Processing Speed ◽  
Processing Time ◽  
State Of The Art ◽  
Proof Automation ◽  
New Knowledge ◽  
Textual Entailment ◽  
Amount Of Knowledge ◽  
Recognizing Textual Entailment

In logic-based approaches to reasoning tasks such as Recognizing Textual Entailment (RTE), it is important for a system to have a large amount of knowledge data. However, there is a tradeoff between adding more knowledge data for improved RTE performance and maintaining an efficient RTE system, as such a big database is problematic in terms of the memory usage and computational complexity. In this work, we show the processing time of a state-of-the-art logic-based RTE system can be significantly reduced by replacing its search-based axiom injection (abduction) mechanism by that based on Knowledge Base Completion (KBC). We integrate this mechanism in a Coq plugin that provides a proof automation tactic for natural language inference. Additionally, we show empirically that adding new knowledge data contributes to better RTE performance while not harming the processing speed in this framework.

scholarly journals Knowledge-Based Textual Inference via Parse-Tree Transformations

2015 ◽  
Vol 54 ◽  
pp. 1-57 ◽  
Author(s):  
Roy Bar-Haim ◽  
Ido Dagan ◽  
Jonathan Berant
Keyword(s):  
Natural Language ◽  
Relation Extraction ◽  
Practical Applications ◽  
Knowledge Based ◽  
Tree Transformations ◽  
Textual Entailment ◽  
Automatic Methods ◽  
Parse Trees ◽  
Recognizing Textual Entailment ◽  
Meaning Representation

Textual inference is an important component in many applications for understanding natural language. Classical approaches to textual inference rely on logical representations for meaning, which may be regarded as "external" to the natural language itself. However, practical applications usually adopt shallower lexical or lexical-syntactic representations, which correspond closely to language structure. In many cases, such approaches lack a principled meaning representation and inference framework. We describe an inference formalism that operates directly on language-based structures, particularly syntactic parse trees. New trees are generated by applying inference rules, which provide a unified representation for varying types of inferences. We use manual and automatic methods to generate these rules, which cover generic linguistic structures as well as specific lexical-based inferences. We also present a novel packed data-structure and a corresponding inference algorithm that allows efficient implementation of this formalism. We proved the correctness of the new algorithm and established its efficiency analytically and empirically. The utility of our approach was illustrated on two tasks: unsupervised relation extraction from a large corpus, and the Recognizing Textual Entailment (RTE) benchmarks.

scholarly journals Inherent Disagreements in Human Textual Inferences

2019 ◽  
Vol 7 ◽  
pp. 677-694
Author(s):  
Ellie Pavlick ◽  
Tom Kwiatkowski
Keyword(s):  
Natural Language ◽  
State Of The Art ◽  
Current State ◽  
Textual Entailment ◽  
Recognizing Textual Entailment

We analyze human’s disagreements about the validity of natural language inferences. We show that, very often, disagreements are not dismissible as annotation “noise”, but rather persist as we collect more ratings and as we vary the amount of context provided to raters. We further show that the type of uncertainty captured by current state-of-the-art models for natural language inference is not reflective of the type of uncertainty present in human disagreements. We discuss implications of our results in relation to the recognizing textual entailment (RTE)/natural language inference (NLI) task. We argue for a refined evaluation objective that requires models to explicitly capture the full distribution of plausible human judgments.

scholarly journals Ordinal Common-sense Inference

2017 ◽  
Vol 5 ◽  
pp. 379-395 ◽  
Author(s):  
Sheng Zhang ◽  
Rachel Rudinger ◽  
Kevin Duh ◽  
Benjamin Van Durme
Keyword(s):  
Natural Language ◽  
Common Sense ◽  
Textual Entailment ◽  
Recognizing Textual Entailment ◽  
Common Sense Knowledge

Humans have the capacity to draw common-sense inferences from natural language: various things that are likely but not certain to hold based on established discourse, and are rarely stated explicitly. We propose an evaluation of automated common-sense inference based on an extension of recognizing textual entailment: predicting ordinal human responses on the subjective likelihood of an inference holding in a given context. We describe a framework for extracting common-sense knowledge from corpora, which is then used to construct a dataset for this ordinal entailment task. We train a neural sequence-to-sequence model on this dataset, which we use to score and generate possible inferences. Further, we annotate subsets of previously established datasets via our ordinal annotation protocol in order to then analyze the distinctions between these and what we have constructed.

Recognizing textual entailment: Rational, evaluation and approaches

2009 ◽  
Vol 15 (4) ◽  
pp. i-xvii ◽  
Author(s):  
IDO DAGAN ◽  
BILL DOLAN ◽  
BERNARDO MAGNINI ◽  
DAN ROTH
Keyword(s):  
Natural Language ◽  
Question Answering ◽  
Natural Language Understanding ◽  
Language Understanding ◽  
Knowledge Representations ◽  
Semantic Relationships ◽  
Textual Entailment ◽  
Inference Methods ◽  
Recognizing Textual Entailment ◽  
Core Problem

AbstractThe goal of identifying textual entailment – whether one piece of text can be plausibly inferred from another – has emerged in recent years as a generic core problem in natural language understanding. Work in this area has been largely driven by the PASCAL Recognizing Textual Entailment (RTE) challenges, which are a series of annual competitive meetings. The current work exhibits strong ties to some earlier lines of research, particularly automatic acquisition of paraphrases and lexical semantic relationships and unsupervised inference in applications such as question answering, information extraction and summarization. It has also opened the way to newer lines of research on more involved inference methods, on knowledge representations needed to support this natural language understanding challenge and on the use of learning methods in this context. RTE has fostered an active and growing community of researchers focused on the problem of applied entailment. This special issue of the JNLE provides an opportunity to showcase some of the most important work in this emerging area.

scholarly journals Emerging trends: A gentle introduction to fine-tuning

2021 ◽  
Vol 27 (6) ◽  
pp. 763-778
Author(s):  
Kenneth Ward Church ◽  
Zeyu Chen ◽  
Yanjun Ma
Keyword(s):  
Natural Language ◽  
Language Processing ◽  
Question Answering ◽  
General Purpose ◽  
Fine Tuning ◽  
Language Engineering ◽  
Training Models ◽  
Emerging Trends ◽  
Foundation Model ◽  
Programming Skills

AbstractThe previous Emerging Trends article (Church et al., 2021. Natural Language Engineering27(5), 631–645.) introduced deep nets to poets. Poets is an imperfect metaphor, intended as a gesture toward inclusion. The future for deep nets will benefit by reaching out to a broad audience of potential users, including people with little or no programming skills, and little interest in training models. That paper focused on inference, the use of pre-trained models, as is, without fine-tuning. The goal of this paper is to make fine-tuning more accessible to a broader audience. Since fine-tuning is more challenging than inference, the examples in this paper will require modest programming skills, as well as access to a GPU. Fine-tuning starts with a general purpose base (foundation) model and uses a small training set of labeled data to produce a model for a specific downstream application. There are many examples of fine-tuning in natural language processing (question answering (SQuAD) and GLUE benchmark), as well as vision and speech.

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