waashk/dblp huggingface dataset:
discourse processing of dialogues with multiple threads. in this paper we will present our ongoing work on a plan-based discourse processor developed in the context of the enthusiast spanish to english translation system as part of the janus multi-lingual speech-to-speech translation system. we will demonstrate that theories of discourse which postulate a strict tree structure of discourse on either the intentional or attentional level are not totally adequate for handling spontaneous dialogues. we will present our extension to this approach along with its implementation in our plan-based discourse processor. we will demonstrate that the implementation of our approach outperforms an implementation based on the strict tree structure approach.
an interactive domain independent approach to robust dialogue interpretation. we discuss an interactive approach to robust interpretation in a large scale speech-to-speech translation system. where other interactive approaches to robust interpretation have depended upon domain dependent repair rules, the approach described here operates efficiently without any such hand-coded repair knowledge and yields a 37% reduction in error rate over a corpus of noisy sentences.
identifying relevant prior explanations. when human tutors engage in dialogue, they freely exploit all aspects of the mutually known context, including the previous discourse. utterances that do not draw on previous discourse seem awkward, unnatural, or even incoherent. previous discourse must be taken into account in order to relate new information effectively to recently conveyed material, and to avoid repeating old material that would distract the student from what is new.producing a system that displays such behavior involves finding an efficient way to identify which previous explanations (if any) are relevant to the current explanation task. thus, we are implementing a system that uses a case-based reasoning approach to identify previous situations and explanations that could potentially affect the explanation being constructed. we have identified heuristics for constructing explanations that exploit this information in ways similar to what we have observed in human-human tutorial dialogues.
ures : an unsupervised web relation extraction system. most information extraction systems either use hand written extraction patterns or use a machine learning algorithm that is trained on a manually annotated corpus. both of these approaches require massive human effort and hence prevent information extraction from becoming more widely applicable. in this paper we present ures (unsupervised relation extraction system), which extracts relations from the web in a totally unsupervised way. it takes as input the descriptions of the target relations, which include the names of the predicates, the types of their attributes, and several seed instances of the relations. then the system downloads from the web a large collection of pages that are likely to contain instances of the target relations. from those pages, utilizing the known seed instances, the system learns the relation patterns, which are then used for extraction. we present several experiments in which we learn patterns and extract instances of a set of several common ie relations, comparing several pattern learning and filtering setups. we demonstrate that using simple noun phrase tagger is sufficient as a base for accurate patterns. however, having a named entity recognizer, which is able to recognize the types of the relation attributes significantly, enhances the extraction performance. we also compare our approach with knowitall's fixed generic patterns.
dependencies between student state and speech recognition problems in spoken tutoring dialogues. speech recognition problems are a reality in current spoken dialogue systems. in order to better understand these phenomena, we study dependencies between speech recognition problems and several higher level dialogue factors that define our notion of student state: frustration/anger, certainty and correctness. we apply chi square (x2) analysis to a corpus of speech-based computer tutoring dialogues to discover these dependencies both within and across turns. significant dependencies are combined to produce interesting insights regarding speech recognition problems and to propose new strategies for handling these problems. we also find that tutoring, as a new domain for speech applications, exhibits interesting tradeoffs and new factors to consider for spoken dialogue design.
part of speech tagging using a network of linear separators. we present an architecture and an on-line learning algorithm and apply it to the problem of part-of-speech tagging. the architecture presented, snow, is a network of linear separators in the feature space, utilizing the winnow update algorithm.multiplicative weight-update algorithms such as winnow have been shown to have exceptionally good behavior when applied to very high dimensional problems, and especially when the target concepts depend on only a small subset of the features in the feature space. in this paper we describe an architecture that utilizes this mistake-driven algorithm for multi-class prediction-selecting the part of speech of a word. the experimental analysis presented here provides more evidence to that these algorithms are suitable for natural language problems.the algorithm used is an on-line algorithm: every example is used by the algorithm only once, and is then discarded. this has significance in terms of efficiency, as well as quick adaptation to new contexts.we present an extensive experimental study of our algorithm under various conditions; in particular, it is shown that the algorithm performs comparably to the best known algorithms for pos.
prosodic aids to syntactic and semantic analysis of spoken english. prosody can be useful in resolving certain lexical and structural ambiguities in spoken english. in this paper we present some results of employing two types of prosodic information, namely pitch and pause, to assist syntactic and semantic analysis during parsing.
spoken dialogue management using probabilistic reasoning. spoken dialogue managers have benefited from using stochastic planners such as markov decision processes (mdps). however, so far, mdps do not handle well noisy and ambiguous speech utterances. we use a partially observable markov decision process (pomdp)-style approach to generate dialogue strategies by inverting the notion of dialogue state; the state represents the user's intentions, rather than the system state. we demonstrate that under the same noisy conditions, a pomdp dialogue manager makes fewer mistakes than an mdp dialogue manager. furthermore, as the quality of speech recognition degrades, the pomdp dialogue manager automatically adjusts the policy.
automatic generation of domain models for call-centers from noisy transcriptions. call centers handle customer queries from various domains such as computer sales and support, mobile phones, car rental, etc. each such domain generally has a domain model which is essential to handle customer complaints. these models contain common problem categories, typical customer issues and their solutions, greeting styles. currently these models are manually created over time. towards this, we propose an unsupervised technique to generate domain models automatically from call transcriptions. we use a state of the art automatic speech recognition system to transcribe the calls between agents and customers, which still results in high word error rates (40%) and show that even from these noisy transcriptions of calls we can automatically build a domain model. the domain model is comprised of primarily a topic taxonomy where every node is characterized by topic(s), typical questions-answers (q&as), typical actions and call statistics. we show how such a domain model can be used for topic identification of unseen calls. we also propose applications for aiding agents while handling calls and for agent monitoring based on the domain model.
argumentative feedback: a linguistically-motivated term expansion for information retrieval. we report on the development of a new automatic feedback model to improve information retrieval in digital libraries. our hypothesis is that some particular sentences, selected based on argumentative criteria, can be more useful than others to perform well-known feedback information retrieval tasks. the argumentative model we explore is based on four disjunct classes, which has been very regularly observed in scientific reports: purpose, methods, results, conclusion. to test this hypothesis, we use the rocchio algorithm as baseline. while rocchio selects the features to be added to the original query based on statistical evidence, we propose to base our feature selection also on argumentative criteria. thus, we restrict the expansion on features appearing only in sentences classified into one of our argumentative categories. our results, obtained on the ohsumed collection, show a significant improvement when expansion is based on purpose (mean average precision = +23%) and conclusion (mean average precision = +41%) contents rather than on other argumentative contents. these results suggest that argumentation is an important linguistic dimension that could benefit information retrieval.
clavius: bi-directional parsing for generic multimodal interaction. we introduce a new multi-threaded parsing algorithm on unification grammars designed specifically for multimodal interaction and noisy environments. by lifting some traditional constraints, namely those related to the ordering of constituents, we overcome several difficulties of other systems in this domain. we also present several criteria used in this model to constrain the search process using dynamically loadable scoring functions. some early analyses of our implementation are discussed.
a domain-specific statistical surface realizer. we present a search-based approach to automatic surface realization given a corpus of domain sentences. using heuristic search based on a statistical language model and a structure we introduce called an inheritance table we overgenerate a set of complete syntactic-semantic trees that are consistent with the given semantic structure and have high likelihood relative to the language model. these trees are then lexicalized, linearized, scored, and ranked. this model is being developed to generate real-time navigation instructions.
multipe default inheritance in a unification-based lexicon. a formalism is presented for lexical specification in unification-based grammars which exploits defeasible multiple inheritance to express regularity, sub-regularity, and exceptions in classifying the properties of words. such systems are in the general case intractable; the present proposal represents an attempt to reduce complexity while retaining sufficient expressive power for the task at hand. illustrative examples are given of morphological analyses from english and german.
asymmetry in parsing and generating with unification grammars: case studies from elu. recent developments in generation algorithms have enabled work in unification-based computational linguistics to approach more closely the ideal of grammars as declarative statements of linguistic facts, neutral between analysis and synthesis. from this perspective, however, the situation is still far from perfect; all known methods of generation impose constraints on the grammars they assume.we briefly consider a number of proposals for generation, outlining their consequences for the form of grammars, and then report on experience arising from the addition of a generator to an existing unification environment. the algorithm in question (based on that of shieber et al. (1989)), though among the most permissive currently available, excludes certain classes of parsable analyses.
simultaneous english-japanese spoken language translation based on incremental dependency parsing and transfer. this paper proposes a method for incrementally translating english spoken language into japanese. to realize simultaneous translation between languages with different word order, such as english and japanese, our method utilizes the feature that the word order of a target language is flexible. to resolve the problem of generating a grammatically incorrect sentence, our method uses dependency structures and japanese dependency constraints to determine the word order of a translation. moreover, by considering the fact that the inversion of predicate expressions occurs more frequently in japanese spoken language, our method takes advantage of a predicate inversion to resolve the problem that japanese has the predicate at the end of a sentence. furthermore, our method includes the function of canceling an inversion by restating a predicate when the translation is incomprehensible due to the inversion. we implement a prototype translation system and conduct an experiment with all 578 sentences in the atis corpus. the results indicate improvements in comparison to two other methods.
towards a cognitively plausible model for quantification. the purpose of this paper is to suggest that quantifiers in natural languages do not have a fixed truth functional meaning as has long been held in logical semantics. instead we suggest that quantifiers can best be modeled as complex inference procedures that are highly dynamic and sensitive to the linguistic context, as well as time and memory constraints.
combination of arabic preprocessing schemes for statistical machine translation. statistical machine translation is quite robust when it comes to the choice of input representation. it only requires consistency between training and testing. as a result, there is a wide range of possible preprocessing choices for data used in statistical machine translation. this is even more so for morphologically rich languages such as arabic. in this paper, we study the effect of different word-level preprocessing schemes for arabic on the quality of phrase-based statistical machine translation. we also present and evaluate different methods for combining preprocessing schemes resulting in improved translation quality.
a best-first probabilistic shift-reduce parser. recently proposed deterministic classifier-based parsers (nivre and scholz, 2004; sagae and lavie, 2005; yamada and mat-sumoto, 2003) offer attractive alternatives to generative statistical parsers. deterministic parsers are fast, efficient, and simple to implement, but generally less accurate than optimal (or nearly optimal) statistical parsers. we present a statistical shift-reduce parser that bridges the gap between deterministic and probabilistic parsers. the parsing model is essentially the same as one previously used for deterministic parsing, but the parser performs a best-first search instead of a greedy search. using the standard sections of the wsj corpus of the penn treebank for training and testing, our parser has 88.1% precision and 87.8% recall (using automatically assigned part-of-speech tags). perhaps more interestingly, the parsing model is significantly different from the generative models used by other well-known accurate parsers, allowing for a simple combination that produces precision and recall of 90.9% and 90.7%, respectively.
automatic measurement of syntactic development in child language. to facilitate the use of syntactic information in the study of child language acquisition, a coding scheme for grammatical relations (grs) in transcripts of parent-child dialogs has been proposed by sagae, macwhinney and lavie (2004). we discuss the use of current nlp techniques to produce the grs in this annotation scheme. by using a statistical parser (charniak, 2000) and memory-based learning tools for classification (daelemans et al., 2004), we obtain high precision and recall of several grs. we demonstrate the usefulness of this approach by performing automatic measurements of syntactic development with the index of productive syntax (scarborough, 1990) at similar levels to what child language researchers compute manually.
using linguistic knowledge in automatic abstracting. we present work on the automatic generation of short indicative-informative abstracts of scientific and technical articles. the indicative part of the abstract identifies the topics of the document while the informative part of the abstract elaborate some topics according to the reader's interest by motivating the topics, describing entities and defining concepts. we have defined our method of automatic abstracting by studying a corpus professional abstracts. the method also considers the reader's interest as essential in the process of abstracting.
how do we count? the problem of tagging phrasal verbs in parts. this paper examines the current performance of the stochastic tagger parts (church 88) in handling phrasal verbs, describes a problem that arises from the statistical model used, and suggests a way to improve the tagger's performance. the solution involves a change in the definition of what counts as a word for the purpose of tagging phrasal verbs.
a word-order database for testing computational models of language acquisition. an investment of effort over the last two years has begun to produce a wealth of data concerning computational psycholinguistic models of syntax acquisition. the data is generated by running simulations on a recently completed database of word order patterns from over 3,000 abstract languages. this article presents the design of the database which contains sentence patterns, grammars and derivations that can be used to test acquisition models from widely divergent paradigms. the domain is generated from grammars that are linguistically motivated by current syntactic theory and the sentence patterns have been validated as psychologically/developmentally plausible by checking their frequency of occurrence in corpora of child-directed speech. a small case-study simulation is also presented.
syntactic approaches to automatic book indexing. automatic book indexing systems are based on the generation of phrase structures capable of reflecting text content. some approaches are given for the automatic construction of back-of-book indexes using a syntactic analysis of the available texts, followed by the identification of nominal constructions, the assignment of importance weights to the term phrases, and the choice of phrases as indexing units.
generalized augmented transition network grammars for generation from semantic networks. the augmented transition network (atn) is a formalism for writing parsing grammars that has been much used in artificial intelligence and computational linguistics. a few researchers have also used atns for writing grammars for generating sentences. previously, however, either generation atns did not have the same semantics as parsing atns, or they required an auxiliary mechanism to determine the syntactic structure of the sentence to be generated. this paper reports a generalization of the atn formalism that allows atn grammars to be written to parse labelled directed graphs. specifically, an atn grammar can be written to parse a semantic network and generate a surface string as its analysis. an example is given of a combined parsing-generating grammar that parses surface sentences, builds and queries a semantic network knowledge representation, and generates surface sentences in response.
on the existence of primitive meaning units. knowledge representation schemes are either based on a set of primitives or not. the decision of whether or not to have a primitive-based scheme is crucial since it affects the knowledge that is stored and how that knowledge may be processed. we suggest that a knowledge representation scheme may not initially have primitives, but may evolve into a primitive-based scheme by inferring a set of primitive meaning units based on previous experience. we describe a program that infers its own primitive set and discuss how the inferred primitives may affect the organization of existing information and the subsequent incorporation of new information.
using comparable corpora to solve problems difficult for human translators. in this paper we present a tool that uses comparable corpora to find appropriate translation equivalents for expressions that are considered by translators as difficult. for a phrase in the source language the tool identifies a range of possible expressions used in similar contexts in target language corpora and presents them to the translator as a list of suggestions. in the paper we discuss the method and present results of human evaluation of the performance of the tool, which highlight its usefulness when dictionary solutions are lacking.
dialogue act tagging with transformation-based learning. for the task of recognizing dialogue acts, we are applying the transformation-based learning (tbl) machine learning algorithm. to circumvent a sparse data problem, we extract values of well-motivated features of utterances, such as speaker direction, punctuation marks, and a new feature, called dialogue act cues, which we find to be more effective than cue phrases and word n-grams in practice. we present strategies for constructing a set of dialogue act cues automatically by minimizing the entropy of the distribution of dialogue acts in a training corpus, filtering out irrelevant dialogue act cues, and clustering semantically-related words. in addition, to address limitations of tbl, we introduce a monte carlo strategy for training efficiently and a committee method for computing confidence measures. these ideas are combined in our working implementation, which labels held-out data as accurately as any other reported system for the dialogue act tagging task.
grammar specialization through entropy thresholds. explanation-based generalization is used to extract a specialized grammar from the original one using a training corpus of parse trees. this allows very much faster parsing and gives a lower error rate, at the price of a small loss in coverage. previously, it has been necessary to specify the tree-cutting criteria (or operationality criteria) manually; here they are derived automatically from the training set and the desired coverage of the specialized grammar. this is done by assigning an entropy value to each node in the parse trees and cutting in the nodes with sufficiently high entropy values.
conciseness through aggregation in text generation. aggregating different pieces of similar information is necessary to generate concise and easy to understand reports in technical domains. this paper presents a general algorithm that combines similar messages in order to generate one or more coherent sentences for them. the process is not as trivial as might be expected. problems encountered are briefly described.
comparing a linguistic and a stochastic tagger. concerning different approaches to automatic pos tagging: engcg-2, a constraint-based morphological tagger, is compared in a double-blind test with a state-of-the-art statistical tagger on a common disambiguation task using a common tag set. the experiments show that for the same amount of remaining ambiguity, the error rate of the statistical tagger is one order of magnitude greater than that of the rule-based one. the two related issues of priming effects compromising the results and disagreement between human annotators are also addressed.
segregatory coordination and ellipsis in text generation. in this paper, we provide an account of how to generate sentences with coordination constructions from clause-sized semantic representations. an algorithm is developed and various examples from linguistic literature will be used to demonstrate that the algorithm does its job well.