Papers by Denis Peskov

6 papers
ContraCAT: Contrastive Coreference Analytical Templates for Machine Translation (2020.coling-main)

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Challenge: Recent high scores on pronoun translation suggest current approaches work well . et al., 2018: are context-aware nmt models learning this task?
Approach: They propose a test set to assess the ability to handle specific steps for pronoun translation . they propose heuristics that break down when translations require real reasoning .
Outcome: The proposed model can model complex inferences required for translation of english into german . it shows that current approaches are not able to model all of this information well .
Multi-Domain Goal-Oriented Dialogues (MultiDoGO): Strategies toward Curating and Annotating Large Scale Dialogue Data (D19-1)

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Challenge: a large number of goal-oriented dialogue datasets are limited in their size, linguistic diversity, domain coverage, or annotation granularity.
Approach: They propose a multi-domain goal-oriented dialogue dataset that uses a crowd-sourced worker and a trained annotator to curate and annotate large scale data.
Outcome: The proposed dataset is 8 times the size of the largest comparable dialogue dataset available to the public.
It Takes Two to Lie: One to Lie, and One to Listen (2020.acl-main)

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Challenge: Deception is a powerful tool for predicting when a lie occurs in long-lasting relationships . a functioning society is impossible without trust, but deception can be betrayed through false identities, spearphishing attacks and disinformation campaigns.
Approach: They propose a dataset to analyze the use of deception in online negotiation-based game Diplomacy . it captures deceptions in long-lasting relationships where interlocutors combine truth with lies to advance objectives.
Outcome: The proposed model predicts when a lie occurs nearly as well as human players.
Adapting Entities across Languages and Cultures (2021.findings-emnlp)

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Challenge: a structured knowledge base adapts named entities using their shared properties.
Approach: They propose automatic methods to adapt named entities using shared properties . they compare them to human adaptations using a new dataset of human adaptation data .
Outcome: The proposed methods compare to human adaptations using a new dataset.
Comparing and Developing Tools to Measure the Readability of Domain-Specific Texts (D19-1)

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Challenge: Despite this, we lack a thorough understanding of how to validly measure readability at scale, especially for domain-specific texts.
Approach: They present a comparison of the validity of well-known readability measures and introduce a novel approach to measure readability at scale.
Outcome: The proposed approach addresses shortcomings of existing measures.
Can You Unpack That? Learning to Rewrite Questions-in-Context (D19-1)

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Challenge: Existing QA datasets lack key NLP problems like coreference and ellipsis resolution.
Approach: They propose a task of question-in-context rewriting to rewrite a context-dependent question into a self-contained question with the same answer.
Outcome: The proposed task is based on a dataset of 40,527 questions based in QuAC . it requires models to link questions together to resolve conversational dependencies .

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