Conceptual Annotations Preserve Structure Across Translations: A French-English Case Study

Abstract

Divergence of syntactic structures between languages constitutes a major challenge in using linguistic structure in Machine Translation (MT) systems. Here, we examine the potential of semantic structures. While semantic annotation is appealing as a source of cross-linguistically stable structures, little has been accomplished in demonstrating this stability through a detailed corpus study. In this paper, we experiment with the UCCA conceptual-cognitive annotation scheme in an English-French case study. First, we show that UCCA can be used to annotate French, through a systematic type-level analysis of the major French grammatical phenomena. Second, we annotate a parallel English-French corpus with UCCA, and quantify the similarity of the structures on both sides. Results show a high degree of stability across translations, supporting the usage of semantic annotations over syntactic ones in structure-aware MT systems.

A Transition-Based Directed Acyclic Graph Parser for UCCA

“…It has demonstrated applicability to multiple languages, including English, French, German and Czech, support for rapid annotation by non-experts (assisted by an accessible annotation interface ), and stability under translation (Sulem et al, 2015). It has also proven useful for machine translation evaluation (Birch et al, 2016).…”

The State of the Art in Semantic Representation

“…Another common criterion for evaluating a semantic scheme is invariance, where semantic analysis should be similar across paraphrases or translation pairs (Xue et al, 2014;Sulem et al, 2015). For instance, most SRL schemes abstract away from the syntactic divergence between the sentences (1) "He gave a present to John" and (2) "It was John who was given a present" (although a complete analysis would reflect the difference of focus between them).…”

“…In its current state, UCCA is considerably more coarse-grained than the above mentioned schemes (e.g., it does not include semantic role information). However, its distinctions tend to generalize well across languages (Sulem et al, 2015). For example, unlike AMR, it distinguishes between primary and aspectual verbs, so cases such as "happened to meet" are annotated similarly to cases such as "met by chance", and differently from "asked to meet".…”

Made for Each Other: Broad-Coverage Semantic Structures Meet Preposition Supersenses

“…It aims to represent the main semantic phenomena in the text, abstracting away from syntactic forms. UCCA's foundational layer, which is the only layer annotated over text so far, 3 reflects a coarse-grained level of semantics that has been shown to be preserved remarkably well across translations (Sulem et al, 2015). It has also been successfully used for improving text simplification (Sulem et al, 2018b), as well as to the evaluation of a number of textto-text generation tasks (Birch et al, 2016;Sulem et al, 2018a;Choshen and Abend, 2018).…”