The problem of logical-form equivalence

Engineering and Applied Science

SAsSy – Scrutable Autonomous Systems

Abstract. An autonomous system consists of physical or virtual systems that can perform tasks without continuous human guidance. Autonomous systems are becoming increasingly ubiquitous, ranging from unmanned vehicles, to robotic surgery devices, to virtual agents which collate and process information on the internet. Existing autonomous systems are opaque, limiting their usefulness in many situations. In order to realise their promise, techniques for making such autonomous systems scrutable are therefore required. We believe that the creation of such scrutable autonomous systems rests on four foundations, namely an appropriate planning representation; the use of a human understandable reasoning mechanism, such as argumentation theory; appropriate natural language generation tools to translate logical statements into natural ones; and information presentation techniques to enable the user to cope with the deluge of information that autonomous systems can provide. Each of these foundations has its own unique challenges, as does the integration of all of these into a single system.

Semantic transfer in Verbmobil

This paper is a detailed discussion of semantic transfer in the context of the Verbmobil Machine Translation project. The use of semantic transfer as a translation mechanism is introduced and justified by comparison with alternative approaches. Some criteria for evaluation of transfer frameworks are discussed and a comparison is made of three different approaches to the representation of translation rules or equivalences. This is followed by a discussion of control of application of transfer rules and interaction with a domain description and inference component

Incremental Semantics for Dialogue Processing: Requirements, and a Comparison of Two Approaches

International audienceTruly interactive dialogue systems need to construct meaning on at least a word-byword basis. We propose desiderata for incremental semantics for dialogue models and systems, a task not heretofore attempted thoroughly. After laying out the desirable properties we illustrate how they are met by current approaches, comparing two incremental semantic processing frameworks: Dynamic Syntax enriched with Type Theory with Records (DS-TTR) and Robust Minimal Recursion Semantics with incremental processing (RMRS-IP). We conclude these approaches are not significantly different with regards to their semantic representation construction, however their purported role within semantic models and dialogue models is where they diverge

A uniform architecture for parsing and generation

The use of a single grammar for both parsing and generation is an idea with a certain elegance, the desirability of which several researchers have noted. In this paper, we discuss a more radical possibility: not only can a single grammar be used by different processes engaged in various "directions" of processing, but one and the same language-processing architecture can be used for processing the grammar in the various modes. In particular, parsing and generation can be viewed as two processes engaged in by a single parameterized theorem prover for the logical interpretation of the formalism. We discuss our current implementation of such an architecture, which is parameterized in such a way that it can be used for either purpose with grammars written in the PATR formalism. Furthermore, the architecture allows fine tuning to reflect different processing strategies, including parsing models intended to mimic psycholinguistic phenomena. This tuning allows the parsing system to operate within the same realm of efficiency as previous architectures for parsing alone, but with much greater flexibility for engaging in other processing regimes.Engineering and Applied Science

Incremental Semantics for Dialogue Processing: Requirements and a Comparison of Two Approaches

Hough J, Kennington C, Schlangen D, Ginzburg J. Incremental Semantics for Dialogue Processing: Requirements and a Comparison of Two Approaches. In: Proceedings of the 11th International Conference on Computational Semantics (IWCS) 2015. London; 2015: 206-216.Truly interactive dialogue systems need to construct meaning on at least a word-by-word basis. We propose desiderata for incremental semantics for dialogue models and systems, a task not heretofore attempted thoroughly. After laying out the desirable properties we illustrate how they are met by current approaches, comparing two incremental semantic processing frameworks: Dynamic Syntax enriched with Type Theory with Records (DS-TTR) and Robust Minimal Recursion Semantics with incremental processing (RMRS-IP). We conclude these approaches are not significantly different with regards to their semantic representation construction, however their purported role within semantic models and dialogue models is where they diverge

Approximate text generation from non-hierarchical representations in a declarative framework

This thesis is on Natural Language Generation. It describes a linguistic realisation system that translates the semantic information encoded in a conceptual graph into an English language sentence. The use of a non-hierarchically structured semantic representation (conceptual graphs) and an approximate matching between semantic structures allows us to investigate a more general version of the sentence generation problem where one is not pre-committed to a choice of the syntactically prominent elements in the initial semantics. We show clearly how the semantic structure is declaratively related to linguistically motivated syntactic representation — we use D-Tree Grammars which stem from work on Tree-Adjoining Grammars. The declarative specification of the mapping between semantics and syntax allows for different processing strategies to be exploited. A number of generation strategies have been considered: a pure topdown strategy and a chart-based generation technique which allows partially successful computations to be reused in other branches of the search space. Having a generator with increased paraphrasing power as a consequence of using non-hierarchical input and approximate matching raises the issue whether certain 'better' paraphrases can be generated before others. We investigate preference-based processing in the context of generation

Complexity in Translation. An English-Norwegian Study of Two Text Types

The present study discusses two primary research questions. Firstly, we have tried to investigate to what extent it is possible to compute the actual translation relation found in a selection of English-Norwegian parallel texts. By this we understand the generation of translations with no human intervention, and we assume an approach to machine translation (MT) based on linguistic knowledge. In order to answer this question, a measurement of translational complexity is applied to the parallel texts. Secondly, we have tried to find out if there is a difference in the degree of translational complexity between the two text types, law and fiction, included in the empirical material. The study is a strictly product-oriented approach to complexity in translation: it disregards aspects related to translation methods, and to the cognitive processes behind translation. What we have analysed are intersubjectively available relations between source texts and existing translations. The degree of translational complexity in a given translation task is determined by the types and amounts of information needed to solve it, as well as by the accessibility of these information sources, and the effort required when they are processed. For the purpose of measuring the complexity of the relation between a source text unit and its target correspondent, we apply a set of four correspondence types, organised in a hierarchy reflecting divisions between different linguistic levels, along with a gradual increase in the degree of translational complexity. In type 1, the least complex type, the corresponding strings are pragmatically, semantically, and syntactically equivalent, down to the level of the sequence of word forms. In type 2, source and target string are pragmatically and semantically equivalent, and equivalent with respect to syntactic functions, but there is at least one mismatch in the sequence of constituents or in the use of grammatical form words. Within type 3, source and target string are pragmatically and semantically equivalent, but there is at least one structural difference violating syntactic functional equivalence between the strings. In type 4, there is at least one linguistically non-predictable, semantic discrepancy between source and target string. The correspondence type hierarchy, ranging from 1 to 4, is characterised by an increase with respect to linguistic divergence between source and target string, an increase in the need for information and in the amount of effort required to translate, and a decrease in the extent to which there exist implications between relations of source-target equivalence at different linguistic levels. We assume that there is a translational relation between the inventories of simple and complex linguistic signs in two languages which is predictable, and hence computable, from information about source and target language systems, and about how the systems correspond. Thus, computable translations are predictable from the linguistic information coded in the source text, together with given, general information about the two languages and their interrelations. Further, we regard non-computable translations to be correspondences where it is not possible to predict the target expression from the information encoded in the source expression, together with given, general information about SL and TL and their interrelations. Non-computable translations require access to additional information sources, such as various kinds of general or task-specific extra-linguistic information, or task-specific linguistic information from the context surrounding the source expression. In our approach, correspondences of types 1–3 constitute the domain of linguistically predictable, or computable, translations, whereas type 4 correspondences belong to the non-predictable, or non-computable, domain, where semantic equivalence is not fulfilled. The empirical method involves extracting translationally corresponding strings from parallel texts, and assigning one of the types defined by the correspondence hierarchy to each recorded string pair. The analysis is applied to running text, omitting no parts of it. Thus, the distribution of the four types of translational correspondence within a set of data provides a measurement of the degree of translational complexity in the parallel texts that the data are extracted from. The complexity measurements of this study are meant to show to what extent we assume that an ideal, rule-based MT system could simulate the given translations, and for this reason the finite clause is chosen as the primary unit of analysis. The work of extracting and classifying translational correspondences is done manually as it requires a bilingually competent human analyst. In the present study, the recorded data cover about 68 000 words. They are compiled from six different text pairs: two of them are law texts, and the remaining four are fiction texts. Comparable amounts of text are included for each text type, and both directions of translation are covered. Since the scope of the investigation is limited, we cannot, on the basis of our analysis, generalise about the degree of translational complexity in the chosen text types and in the language pair English-Norwegian. Calculated in terms of string lengths, the complexity measurement across the entire collection of data shows that as little as 44,8% of all recorded string pairs are classified as computable translational correspondences, i.e. as type 1, 2, or 3, and non-computable string pairs of type 4 constitute a majority (55,2%) of the compiled data. On average, the proportion of computable correspondences is 50,2% in the law data, and 39,6% in fiction. In relation to the question whether it would be fruitful to apply automatic translation to the selected texts, we have considered the workload potentially involved in correcting machine output, and in this respect the difference in restrictedness between the two text types is relevant. Within the non-computable correspondences, the frequency of cases exhibiting only one minimal semantic deviation between source and target string is considerably higher among the data extracted from the law texts than among those recorded from fiction. For this reason we tentatively regard the investigated pairs of law texts as representing a text type where tools for automatic translation may be helpful, if the effort required by post-editing is smaller than that of manual translation. This is possibly the case in one of the law text pairs, where 60,9% of the data involve computable translation tasks. In the other pair of law texts the corresponding figure is merely 38,8%, and the potential helpfulness of automatisation would be even more strongly determined by the edit cost. That text might be a task for computer-aided translation, rather than for MT. As regards the investigated fiction texts, it is our view that post-editing of automatically generated translations would be laborious and not cost effective, even in the case of one text pair showing a relatively low degree of translational complexity. Hence, we concur with the common view that the translation of fiction is not a task for MT