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Found 21 papers, 7 papers with code
Bootstrapping Multilingual Semantic Parsers using Large Language Models
Despite cross-lingual generalization demonstrated by pre-trained multilingual models, the translate-train paradigm of transferring English datasets across multiple languages remains to be a key mechanism for training task-specific multilingual models.
QA Is the New KR: Question-Answer Pairs as Knowledge Bases
In this position paper, we propose a new approach to generating a type of knowledge base (KB) from text, based on question generation and entity linking.
Event Linking: Grounding Event Mentions to Wikipedia
Third, we retrain and evaluate two state-of-the-art (SOTA) entity linking models, showing the challenges of event linking, and we propose an event-specific linking system EVELINK to set a competitive result for the new task.
Study Of German Manufacturing Firms: Causal Impact Of European Union Emission Trading Scheme On Firm Behaviour And Economic Performance
The results based on the nonparametric nearest neighbor matching suggest a statistically significant positive effect of the EU ETS on the economic performance of the regulated firms during Phase I of the EU ETS.
Causal Impact Of European Union Emission Trading Scheme On Firm Behaviour And Economic Performance: A Study Of German Manufacturing Firms
In this paper, we estimate the causal impact (i. e. Average Treatment Effect, ATT) of the EU ETS on GHG emissions and firm competitiveness (primarily measured by employment, turnover, and exports levels) by combining a difference-in-differences approach with semi-parametric matching techniques and estimators an to investigate the effect of the EU ETS on the economic performance of these German manufacturing firms using a Stochastic Production Frontier model.
Enforcing Consistency in Weakly Supervised Semantic Parsing
The predominant challenge in weakly supervised semantic parsing is that of spurious programs that evaluate to correct answers for the wrong reasons.
Paired Examples as Indirect Supervision in Latent Decision Models
Such an objective does not require external supervision for the values of the latent output, or even the end task, yet provides an additional training signal to that provided by individual training examples themselves.
What do we expect from Multiple-choice QA Systems?
The recent success of machine learning systems on various QA datasets could be interpreted as a significant improvement in models' language understanding abilities.
Improving Compositional Generalization in Semantic Parsing
Generalization of models to out-of-distribution (OOD) data has captured tremendous attention recently.
Evaluating NLP Models via Contrast Sets
Unfortunately, when a dataset has systematic gaps (e. g., annotation artifacts), these evaluations are misleading: a model can learn simple decision rules that perform well on the test set but do not capture a dataset's intended capabilities.
Overestimation of Syntactic Representation in Neural Language Models
With the advent of powerful neural language models over the last few years, research attention has increasingly focused on what aspects of language they represent that make them so successful.
Obtaining Faithful Interpretations from Compositional Neural Networks
Neural module networks (NMNs) are a popular approach for modeling compositionality: they achieve high accuracy when applied to problems in language and vision, while reflecting the compositional structure of the problem in the network architecture.
Overestimation of Syntactic Representationin Neural Language Models
With the advent of powerful neural language models over the last few years, research attention has increasingly focused on what aspects of language they represent that make them so successful.
Evaluating Models' Local Decision Boundaries via Contrast Sets
Unfortunately, when a dataset has systematic gaps (e. g., annotation artifacts), these evaluations are misleading: a model can learn simple decision rules that perform well on the test set but do not capture a dataset's intended capabilities.
Robust Named Entity Recognition with Truecasing Pretraining
Although modern named entity recognition (NER) systems show impressive performance on standard datasets, they perform poorly when presented with noisy data.
Ranked #9 on
Named Entity Recognition (NER)
on WNUT 2017
Neural Module Networks for Reasoning over Text
Answering compositional questions that require multiple steps of reasoning against text is challenging, especially when they involve discrete, symbolic operations.
Joint Multilingual Supervision for Cross-lingual Entity Linking
This enables our approach to: (a) augment the limited supervision in the target language with additional supervision from a high-resource language (like English), and (b) train a single entity linking model for multiple languages, improving upon individually trained models for each language.
Neural Compositional Denotational Semantics for Question Answering
Answering compositional questions requiring multi-step reasoning is challenging.
Entity Linking via Joint Encoding of Types, Descriptions, and Context
For accurate entity linking, we need to capture various information aspects of an entity, such as its description in a KB, contexts in which it is mentioned, and structured knowledge.
Revisiting the Evaluation for Cross Document Event Coreference
Cross document event coreference (CDEC) is an important task that aims at aggregating event-related information across multiple documents.
Collectively Embedding Multi-Relational Data for Predicting User Preferences
Matrix factorization has found incredible success and widespread application as a collaborative filtering based approach to recommendations.
