waashk/dblp huggingface dataset:
dependency-based statistical machine translation. we present a czech-english statistical machine translation system which performs tree-to-tree translation of dependency structures. the only bilingual resource required is a sentence-aligned parallel corpus. all other resources are monolingual. we also refer to an evaluation method and plan to compare our system's output with a benchmark system.
two diverse systems built using generic components for spoken dialogue (recent progress on trips). this paper describes recent progress on the trips architecture for developing spoken-language dialogue systems. the interactive poster session will include demonstrations of two systems built using trips: a computer purchasing assistant, and an object placement (and manipulation) task.
prosody, syntax and parsing. we describe the modification of a grammar to take advantage of prosodic information provided by a speech recognition system. this initial study is limited to the use of relative duration of phonetic segments in the assignment of syntactic structure, specifically in ruling out alternative parses in otherwise ambiguous sentences. taking advantage of prosodic information in parsing can make a spoken language system more accurate and more efficient, if prosodic-syntactic mismatches, or unlikely matches, can be pruned. we know of no other work that has succeeded in automatically extracting speech information and using it in a parser to rule out extraneous parses.
a robust system for natural spoken dialogue. this paper describes a system that leads us to believe in the feasibility of constructing natural spoken dialogue systems in task-oriented domains. it specifically addresses the issue of robust interpretation of speech in the presence of recognition errors. robustness is achieved by a combination of statistical error post-correction, syntactically- and semantically-driven robust parsing, and extensive use of the dialogue context. we present an evaluation of the system using time-to-completion and the quality of the final solution that suggests that most native speakers of english can use the system successfully with virtually no training.
k-valued non-associative lambek categorial grammars are not learnable from strings. this paper is concerned with learning categorial grammars in gold's model. in contrast to k-valued classical categorial grammars, k-valued lambek grammars are not learnable from strings. this result was shown for several variants but the question was left open for the weakest one, the non-associative variant nl.we show that the class of rigid and k-valued nl grammars is unlearnable from strings, for each k; this result is obtained by a specific construction of a limit point in the considered class, that does not use product operator.another interest of our construction is that it provides limit points for the whole hierarchy of lambek grammars, including the recent pregroup grammars.such a result aims at clarifying the possible directions for future learning algorithms: it expresses the difficulty of learning categorial grammars from strings and the need for an adequate structure on examples.
head automata and bilingual tiling: translation with minimal representations. we present a language model consisting of a collection of costed bidirectional finite state automata associated with the head words of phrases. the model is suitable for incremental application of lexical associations in a dynamic programming search for optimal dependency tree derivations. we also present a model and algorithm for machine translation involving optimal "tiling" of a dependency tree with entries of a costed bilingual lexicon. experimental results are reported comparing methods for assigning cost functions to these models. we conclude with a discussion of the adequacy of annotated linguistic strings as representations for machine translation.
tagging unknown proper names using decision trees. this paper describes a supervised learning method to automatically select from a set of noun phrases, embedding proper names of different semantic classes, their most distinctive features. the result of the learning process is a decision tree which classifies an unknown proper name on the basis of its context of occurrence. this classifier is used to estimate the probability distribution of an out of vocabulary proper name over a tagset. this probability distribution is itself used to estimate the parameters of a stochastic part of speech tagger.
automatic acquisition of hierarchical transduction models for machine translation. we describe a method for the fully automatic learning of hierarchical finite state translation models. the input to the method is transcribed speech utterances and their corresponding human translations, and the output is a set of head transducers, i.e. statistical lexical head-outward transducers. a word-alignment function and a head-ranking function are first obtained, and then counts are generated for hypothesized state transitions of head transducers whose lexical translations and word order changes are consistent with the alignment. the method has been applied to create an english-spanish translation model for a speech translation application, with word accuracy of over 75% as measured by a string-distance comparison to three reference translations.
an efficient kernel for multilingual generation in speech-to-speech dialogue translation. we present core aspects of a fully implemented generation component in a multilingual speech-to-speech dialogue translation system. its design was particularly influenced by the necessity of real-time processing and usability for multiple languages and domains. we developed a general kernel system comprising a microplanning and a syntactic realizer module. the microplanner performs lexical and syntactic choice, based on constraint-satisfaction techniques. the syntactic realizer processes hpsg grammars reflecting the latest developments of the underlying linguistic theory, utilizing their pre-processing into the tag formalism. the declarative nature of the knowledge bases, i.e., the microplanning constraints and the hpsg grammars allowed an easy adaption to new domains and languages. the successful integration of our component into the translation system verbmobil proved the fulfillment of the specific real-time constraints.
a comparison of head transducers and transfer for a limited domain translation application. we compare the effectiveness of two related machine translation models applied to the same limited-domain task. one is a transfer model with monolingual head automata for analysis and generation; the other is a direct transduction model based on bilingual head transducers. we conclude that the head transducer model is more effective according to measures of accuracy, computational requirements, model size, and development effort.
the sammie system: multimodal in-car dialogue. the sammie system is an in-car multi-modal dialogue system for an mp3 application. it is used as a testing environment for our research in natural, intuitive mixed-initiative interaction, with particular emphasis on multimodal output planning and realization aimed to produce output adapted to the context, including the driver's attention state w.r.t. the primary driving task.
monotonic semantic interpretation. aspects of semantic interpretation, such as quantifier scoping and reference resolution, are often realised computationally by non-monotonic operations involving loss of information and destructive manipulation of semantic representations. the paper describes how monotonic reference resolution and scoping can be carried out using a revised quasi logical form (qlf) representation. semantics for qlf are presented in which the denotations of formulas are extended monotonically as qlf expressions are resolved.
the rhythm of lexical stress in prose. "prose rhythm" is a widely observed but scarcely quantified phenomenon. we describe an information-theoretic model for measuring the regularity of lexical stress in english texts, and use it in combination with trigram language models to demonstrate a relationship between the probability of word sequences in english and the amount of rhythm present in them. we find that the stream of lexical stress in text from the wall street journal has an entropy rate of less than 0.75 bits per syllable for common sentences. we observe that the average number of syllables per word is greater for rarer word sequences, and to normalize for this effect we run control experiments to show that the choice of word order contributes significantly to stress regularity, and increasingly with lexical probability.
a model of lexical attraction and repulsion. this paper introduces new methods based on exponential families for modeling the correlations between words in text and speech. while previous work assumed the effects of word co-occurrence statistics to be constant over a window of several hundred words, we show that their influence is nonstationary on a much smaller time scale. empirical data drawn from english and japanese text, as well as conversational speech, reveals that the "attraction" between words decays exponentially, while stylistic and syntactic contraints create a "repulsion" between words that discourages close co-occurrence. we show that these characteristics are well described by simple mixture models based on two-stage exponential distributions which can be trained using the em algorithm. the resulting distance distributions can then be incorporated as penalizing features in an exponential language model.
logical forms in the core language engine. this paper describes a 'logical form' target language for representing the literal meaning of english sentences, and an intermediate level of representation ('quasi logical form') which engenders a natural separation between the compositional semantics and the processes of scoping and reference resolution. the approach has been implemented in the sri core language engine which handles the english constructions discussed in the paper.
consonant spreading in arabic stems. this paper examines the phenomenon of consonant spreading in arabic stems. each spreading involves a local surface copying of an underlying consonant, and, in certain phonological contexts, spreading alternates productively with consonant lengthening (or gemination). the morphophonemic triggers of spreading lie in the patterns or even in the roots themselves, and the combination of a spreading root and a spreading pattern causes a consonant to be copied multiple times. the interdigitation of arabic stems and the realization of consonant spreading are formalized using finite-state morphotactics and variation rules, and this approach has been successfully implemented in a large-scale arabic morphological analyzer which is available for testing on the internet.
computing locally coherent discourses. we present the first algorithm that computes optimal orderings of sentences into a locally coherent discourse. the algorithm runs very efficiently on a variety of coherence measures from the literature. we also show that the discourse ordering problem is np-complete and cannot be approximated.
finite-state non-concatenative morphotactics. we describe a new technique for constructing finite-state transducers that involves reapplying the regular-expression compiler to its own output. implemented in an algorithm called compile-replace, this technique has proved useful for handling non-concatenative phenomena; and we demonstrate it on malay full-stem reduplication and arabic stem interdigitation.
the selection of the most probable dependency structure in japanese using mutual information. we use a statistical method to select the most probable structure or parse for a given sentence. it takes as input the dependency structures generated for the sentence by a dependency grammar, finds all triple of modifier, particle and modificant relations, calculates mutual information of each relation and chooses the structure for which the product of the mutual information of its relations is the highest.
inside-outside estimation of a lexicalized pcfg for german. the paper describes an extensive experiment in inside-outside estimation of a lexicalized probabilistic context free grammar for german verb-final clauses. grammar and formalism features which make the experiment feasible are described. successive models are evaluated on precision and recall of phrase markup.
improvement of a whole sentence maximum entropy language model using grammatical features. in this paper, we propose adding long-term grammatical information in a whole sentence maximun entropy language model (wsme) in order to improve the performance of the model. the grammatical information was added to the wsme model as features and were obtained from a stochastic context-free grammar. finally, experiments using a part of the penn treebank corpus were carried out and significant improvements were acheived.
the sentimental factor: improving review classification via human-provided information. sentiment classification is the task of labeling a review document according to the polarity of its prevailing opinion (favorable or unfavorable). in approaching this problem, a model builder often has three sources of information available: a small collection of labeled documents, a large collection of unlabeled documents, and human understanding of language. ideally, a learning method will utilize all three sources. to accomplish this goal, we generalize an existing procedure that uses the latter two.we extend this procedure by re-interpreting it as a naive bayes model for document sentiment. viewed as such, it can also be seen to extract a pair of derived features that are linearly combined to predict sentiment. this perspective allows us to improve upon previous methods, primarily through two strategies: incorporating additional derived features into the model and, where possible, using labeled data to estimate their relative influence.
mt evaluation: human-like vs. human acceptable. we present a comparative study on machine translation evaluation according to two different criteria: human likeness and human acceptability. we provide empirical evidence that there is a relationship between these two kinds of evaluation: human likeness implies human acceptability but the reverse is not true. from the point of view of automatic evaluation this implies that metrics based on human likeness are more reliable for system tuning. our results also show that current evaluation metrics are not always able to distinguish between automatic and human translations. in order to improve the descriptive power of current metrics we propose the use of additional syntax-based metrics, and metric combinations inside the qarla framework.
an empirical study of information synthesis task. this paper describes an empirical study of the "information synthesis" task, defined as the process of (given a complex information need) extracting, organizing and inter-relating the pieces of information contained in a set of relevant documents, in order to obtain a comprehensive, non redundant report that satisfies the information need.two main results are presented: a) the creation of an information synthesis testbed with 72 reports manually generated by nine subjects for eight complex topics with 100 relevant documents each; and b) an empirical comparison of similarity metrics between reports, under the hypothesis that the best metric is the one that best distinguishes between manual and automatically generated reports. a metric based on key concepts overlap gives better results than metrics based on n-gram overlap (such as rouge) or sentence overlap.
qarla: a framework for the evaluation of text summarization systems. this paper presents a probabilistic framework, qarla, for the evaluation of text summarisation systems. the input of the framework is a set of manual (reference) summaries, a set of baseline (automatic) summaries and a set of similarity metrics between summaries. it provides i) a measure to evaluate the quality of any set of similarity metrics, ii) a measure to evaluate the quality of a summary using an optimal set of similarity metrics, and iii) a measure to evaluate whether the set of baseline summaries is reliable or may produce biased results.compared to previous approaches, our framework is able to combine different metrics and evaluate the quality of a set of metrics without any a-priori weighting of their relative importance. we provide quantitative evidence about the effectiveness of the approach to improve the automatic evaluation of text summarisation systems by combining several similarity metrics.
discovering phonotactic finite-state automata by generic search. this paper presents a genetic algorithm based approach to the automatic discovery of finite-state automata (fsas) from positive data. fsas are commonly used in computational phonology, but-given the limited learnability of fsas from arbitrary language subsets-are usually constructed manually. the approach presented here offers a practical automatic method that helps reduce the cost of manual fsa construction.
tense and connective constraints on the expression of causality. starting from descriptions of french connectives (in particular "donc"---therefore), on the one hand, and aspectual properties of french tenses pass&eacute; simple and imparfait on the other hand, we study in this paper how the two interact with respect to the expression of causality. it turns out that their interaction is not free. some combinations are not acceptable, and we propose an explanation for them. these results apply straightforwardly to natural language generation: given as input two events related by a cause relation, we can choose among various ways of presentation (the parameters being (i) the order, (ii) the connective, (iii) the tense) so that we are sure to express a cause relation, without generating either an incorrect discourse or an ambiguous one.
query-relevant summarization using faqs. this paper introduces a statistical model for query-relevant summarization: succinctly characterizing the relevance of a document to a query. learning parameter values for the proposed model requires a large collection of summarized documents, which we do not have, but as a proxy, we use a collection of faq (frequently-asked question) documents. taking a learning approach enables a principled, quantitative evaluation of the proposed system, and the results of some initial experiments---on a collection of usenet faqs and on a faq-like set of customer-submitted questions to several large retail companies---suggest the plausibility of learning for summarization.
time mapping with hypergraphs. word graphs are able to represent a large number of different utterance hypotheses in a very compact manner. however, usually they contain a huge amount of redundancy in terms of word hypotheses that cover almost identical intervals in time. we address this problem by introducing hypergraphs for speech processing. hypergraphs can be classified as an extension to word graphs and charts, their edges possibly having several start and end vertices. by converting ordinary word graphs to hypergraphs one can reduce the number of edges considerably. we define hypergraphs formally, present an algorithm to convert word graphs into hypergraphs and state consistency properties for edges and their combination. finally, we present some empirical results concerning graph size and parsing effciency.
bootstrapping path-based pronoun resolution. we present an approach to pronoun resolution based on syntactic paths. through a simple bootstrapping procedure, we learn the likelihood of coreference between a pronoun and a candidate noun based on the path in the parse tree between the two entities. this path information enables us to handle previously challenging resolution instances, and also robustly addresses traditional syntactic coreference constraints. highly coreferent paths also allow mining of precise probabilistic gender/number information. we combine statistical knowledge with well known features in a support vector machine pronoun resolution classifier. significant gains in performance are observed on several datasets.
a high-performance semi-supervised learning method for text chunking. in machine learning, whether one can build a more accurate classifier by using unlabeled data (semi-supervised learning) is an important issue. although a number of semi-supervised methods have been proposed, their effectiveness on nlp tasks is not always clear. this paper presents a novel semi-supervised method that employs a learning paradigm which we call structural learning. the idea is to find "what good classifiers are like" by learning from thousands of automatically generated auxiliary classification problems on unlabeled data. by doing so, the common predictive structure shared by the multiple classification problems can be discovered, which can then be used to improve performance on the target problem. the method produces performance higher than the previous best results on conll'00 syntactic chunking and conll'03 named entity chunking (english and german).
finding parts in very large corpora. we present a method for extracting parts of objects from wholes (e.g. "speedometer" from "car"). given a very large corpus our method finds part words with 55% accuracy for the top 50 words as ranked by the system. the part list could be scanned by an end-user and added to an existing ontology (such as wordnet), or used as a part of a rough semantic lexicon.
resolution of collective-distributive ambiguity using model-based reasoning. i present a semantic analysis of collective-distributive ambiguity, and resolution of such ambiguity by model-based reasoning. this approach goes beyond scha and stallard [17], whose reasoning capability was limited to checking semantic types. my semantic analysis is based on link [14, 13] and roberts [15], where distributivity comes uniformly from a quantificational operator, either explicit (e.g. each) or implicit (e.g. the d operator). i view the semantics module of the natural language system as a hypothesis generator and the reasoner in the pragmatics module as a hypothesis filter (cf. simmons and davis [18]). the reasoner utilizes a model consisting of domain-dependent constraints and domain-independent axioms for disambiguation. there are two kinds of constraints, type constraints and numerical constraints, and they are associated with predicates in the knowledge base. whenever additional information is derived from the model, the contradiction checker is invoked to detect any contradiction in a hypothesis using simple mathematical knowledge. cdcl (collective-distributive constraint language) is used to represent hypotheses, constraints, and axioms in a way isomorphic to diagram representations of collective-distributive ambiguity.
evaluating automated and manual acquisition of anaphora resolution strategies. we describe one approach to build an automatically trainable anaphora resolution system. in this approach, we use japanese newspaper articles tagged with discourse information as training examples for a machine learning algorithm which employs the c4.5 decision tree algorithm by quinlan (quinlan, 1993). then, we evaluate and compare the results of several variants of the machine learning-based approach with those of our existing anaphora resolution system which uses manually-designed knowledge sources. finally, we compare our algorithms with existing theories of anaphora, in particular, japanese zero pronouns.
a language-independent anaphora resolution system for understanding multilingual texts. this paper describes a new discourse module within our multilingual nlp system. because of its unique data-driven architecture, the discourse module is language-independent. moreover, the use of hierarchically organized multiple knowledge sources makes the module robust and trainable using discourse-tagged corpora. separating discourse phenomena from knowledge sources makes the discourse module easily extensible to additional phenomena.