Parallel Data Helps Neural Entity Coreference Resolution

Coreference resolution is the task of finding expressions that refer to the same entity in a text. Coreference models are generally trained on monolingual annotated data but annotating coreference is expensive and challenging. Hardmeier et al.(2013) have shown that parallel data contains latent anaphoric knowledge, but it has not been explored in end-to-end neural models yet. In this paper, we propose a simple yet effective model to exploit coreference knowledge from parallel data. In addition to the conventional modules learning coreference from annotations, we introduce an unsupervised module to capture cross-lingual coreference knowledge. Our proposed cross-lingual model achieves consistent improvements, up to 1.74 percentage points, on the OntoNotes 5.0 English dataset using 9 different synthetic parallel datasets. These experimental results confirm that parallel data can provide additional coreference knowledge which is beneficial to coreference resolution tasks.Comment: camera-ready version; to appear in the Findings of ACL 202

A Document-Level SMT System with Integrated Pronoun Prediction

This paper describes one of Uppsala University’s submissions to the pronoun-focused machine translation (MT) shared task at DiscoMT 2015. The system is based on phrase-based statistical MT implemented with the document-level decoder Docent. It includes a neural network for pronoun prediction trained with latent anaphora resolution. At translation time, coreference information is obtained from the Stanford CoreNLP system

Exploring Predictive Uncertainty and Calibration in NLP: A Study on the Impact of Method & Data Scarcity

We investigate the problem of determining the predictive confidence (or, conversely, uncertainty) of a neural classifier through the lens of low-resource languages. By training models on sub-sampled datasets in three different languages, we assess the quality of estimates from a wide array of approaches and their dependence on the amount of available data. We find that while approaches based on pre-trained models and ensembles achieve the best results overall, the quality of uncertainty estimates can surprisingly suffer with more data. We also perform a qualitative analysis of uncertainties on sequences, discovering that a model's total uncertainty seems to be influenced to a large degree by its data uncertainty, not model uncertainty. All model implementations are open-sourced in a software package

On Statistical Machine Translation and Translation Theory

The translation process in statistical machine translation (SMT) is shaped by technical constraints and engineering considerations. SMT explicitly models translation as search for a target-language equivalent of the input text. This perspective on translation had wide currency in mid-20th century translation studies, but has since been superseded by approaches arguing for a more complex relation between source and target text. In this paper, we show how traditional assumptions of translational equivalence are embodied in SMT through the concepts of word alignment and domain and discuss some limitations arising from the word-level/corpus-level dichotomy inherent in these concepts

On Statistical Machine Translation and Translation Theory

Abstract The translation process in statistical machine translation (SMT) is shaped by technical constraints and engineering considerations. SMT explicitly models translation as search for a target-language equivalent of the input text. This perspective on translation had wide currency in mid-20th century translation studies, but has since been superseded by approaches arguing for a more complex relation between source and target text. In this paper, we show how traditional assumptions of translational equivalence are embodied in SMT through the concepts of word alignment and domain and discuss some limitations arising from the word-level/corpus-level dichotomy inherent in these concepts

Improving Machine Translation Quality Prediction with Syntactic Tree Kernels

We investigate the problem of predicting the quality of a given Machine Translation (MT) output segment as a binary classification task. In a study with four different data sets in two text genres and two language pairs, we show that the performance of a Support Vector Machine (SVM) classifier can be improved by extending the feature set with implicitly defined syntactic features in the form of tree kernels over syntactic parse trees. Moreover, we demonstrate that syntax tree kernels achieve surprisingly high performance levels even without additional features, which makes them suitable as a low-effort initial building block for an MT quality estimation system