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NAACL 2021 2021 (115)
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Hits 1 – 20 of 115
1
Negative language transfer in learner English: A new dataset ...
NAACL 2021 2021
;
Demmans Epp, Carrie
;
Farias Wanderley, Leticia
. - : Underline Science Inc., 2021
BASE
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2
Generative Imagination Elevates Machine Translation ...
NAACL 2021 2021
;
Li, Lei
;
Long, Quanyu
. - : Underline Science Inc., 2021
BASE
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3
Parallel sentences mining with transfer learning in an unsupervised setting ...
NAACL 2021 2021
;
Feng, Yifan
;
Mi, Chenggang
. - : Underline Science Inc., 2021
BASE
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4
Source and Target Bidirectional Knowledge Distillation for End-to-end Speech Translation ...
NAACL 2021 2021
;
Inaguma, Hirofumi
. - : Underline Science Inc., 2021
BASE
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5
Detoxifying Language Models Risks Marginalizing Minority Voices ...
NAACL 2021 2021
;
Gururangan, Suchin
;
Klein, Dan
. - : Underline Science Inc., 2021
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6
Domain Adaptation for Arabic Cross-Domain and Cross-Dialect Sentiment Analysis from Contextualized Word Embedding ...
NAACL 2021 2021
;
Berrada, Ismail
;
El Mahdaouy, Abdelkader
. - : Underline Science Inc., 2021
BASE
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7
Knowledge Enhanced Masked Language Model for Stance Detection ...
NAACL 2021 2021
;
Kawintiranon, Kornraphop
;
Singh, Lisa
. - : Underline Science Inc., 2021
BASE
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8
LIVE SESSION: 15D-Oral: Phonology, Morphology and Word Segmentation ...
NAACL 2021 2021
. - : Underline Science Inc., 2021
BASE
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9
Universal Dependencies ...
NAACL 2021 2021
;
de Marneffe, Marie-Catherine
;
Manning, Christopher
. - : Underline Science Inc., 2021
BASE
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10
How low is too low? A monolingual take on lemmatisation in Indian languages ...
NAACL 2021 2021
;
Bhattacharyya, Pushpak
;
Saunack, Kumar
. - : Underline Science Inc., 2021
BASE
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11
Frustratingly Easy Edit-based Linguistic Steganography with a Masked Language Model ...
NAACL 2021 2021
;
Kurohashi, Sadao
;
Murawaki, Yugo
. - : Underline Science Inc., 2021
BASE
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12
MelBERT: Metaphor Detection via Contextualized Late Interaction using Metaphorical Identification Theories ...
NAACL 2021 2021
;
Choi, Minjin
;
Choi, Eunseong
. - : Underline Science Inc., 2021
BASE
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13
A Global Past-Future Early Exit Method for Accelerating Inference of Pre-trained Language Models ...
NAACL 2021 2021
;
He, Bin
;
Liao, Kaiyuan
. - : Underline Science Inc., 2021
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14
DirectProbe: Studying Representations without Classifiers ...
NAACL 2021 2021
;
Srikumar, Vivek
;
Zhou, Yichu
. - : Underline Science Inc., 2021
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15
Challenging distributional models with a conceptual network of philosophical terms ...
NAACL 2021 2021
;
Bloem, Jelke
;
Fokkens, Antske
. - : Underline Science Inc., 2021
BASE
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16
Modeling the Severity of Complaints in Social Media ...
NAACL 2021 2021
;
Aletras, Nikolaos
;
Jin, Mali
. - : Underline Science Inc., 2021
BASE
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17
ERNIE-Gram: Pre-Training with Explicitly N-Gram Masked Language Modeling for Natural Language Understanding ...
NAACL 2021 2021
;
Li, Yu-Kun
;
Xiao, Dongling
. - : Underline Science Inc., 2021
BASE
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18
Proteno: Text Normalization with Limited Data for Fast Deployment in Text to Speech Systems ...
NAACL 2021 2021
;
Tyagi, Shubhi
. - : Underline Science Inc., 2021
BASE
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19
CaSiNo: A Corpus of Campsite Negotiation Dialogues for Automatic Negotiation Systems ...
NAACL 2021 2021
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Chawla, Kushal
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Clever, Rene
. - : Underline Science Inc., 2021
BASE
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20
multiPRover: Generating Multiple Proofs for Improved Interpretability in Rule Reasoning ...
NAACL 2021 2021
;
Bansal, Mohit
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Saha, Swarnadeep
;
Yadav, Prateek
. - : Underline Science Inc., 2021
Abstract:
Read the paper on the folowing link: https://www.aclweb.org/anthology/2021.naacl-main.287/ Abstract: We focus on a type of linguistic formal reasoning where the goal is to reason over explicit knowledge in the form of natural language facts and rules (Clark et al. 2020). A recent work, named PRover (Saha et al. 2020), performs such reasoning by answering a question and also generating a proof graph that explains the answer. However, compositional reasoning is not always unique and there may be multiple ways of reaching the correct answer. Thus, in our work, we address a new and challenging problem of generating multiple proof graphs for reasoning over natural language rule-bases. Each proof provides a different rationale for the answer, thereby improving the interpretability of such reasoning systems. In order to jointly learn from all proof graphs and exploit the correlations between multiple proofs for a question, we pose this task as a set generation problem over structured output spaces where each proof ...
Keyword:
Artificial Intelligence
;
Computer Science and Engineering
;
Intelligent System
;
Natural Language Processing
URL:
https://underline.io/lecture/19592-multiprover-generating-multiple-proofs-for-improved-interpretability-in-rule-reasoning
https://dx.doi.org/10.48448/c20y-hz48
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