waashk/dblp huggingface dataset:
scaling phrase-based statistical machine translation to larger corpora and longer phrases. in this paper we describe a novel data structure for phrase-based statistical machine translation which allows for the retrieval of arbitrarily long phrases while simultaneously using less memory than is required by current decoder implementations. we detail the computational complexity and average retrieval times for looking up phrase translations in our suffix array-based data structure. we show how sampling can be used to reduce the retrieval time by orders of magnitude with no loss in translation quality.
statistical machine translation with word- and sentence-aligned parallel corpora. the parameters of statistical translation models are typically estimated from sentence-aligned parallel corpora. we show that significant improvements in the alignment and translation quality of such models can be achieved by additionally including word-aligned data during training. incorporating word-level alignments into the parameter estimation of the ibm models reduces alignment error rate and increases the bleu score when compared to training the same models only on sentence-aligned data. on the verbmobil data set, we attain a 38% reduction in the alignment error rate and a higher bleu score with half as many training examples. we discuss how varying the ratio of word-aligned to sentence-aligned data affects the expected performance gain.
using linguistic principles to recover empty categories. this paper describes an algorithm for detecting empty nodes in the penn treebank (marcus et al., 1993), finding their antecedents, and assigning them function tags, without access to lexical information such as valency. unlike previous approaches to this task, the current method is not corpus-based, but rather makes use of the principles of early government-binding theory (chomsky, 1981), the syntactic theory that underlies the annotation. using the evaluation metric proposed by johnson (2002), this approach outperforms previously published approaches on both detection of empty categories and antecedent identification, given either annotated input stripped of empty categories or the output of a parser. some problems with this evaluation metric are noted and an alternative is proposed along with the results. the paper considers the reasons a principle-based approach to this problem should outperform corpus-based approaches, and speculates on the possibility of a hybrid approach.
generating an ltag out of a principle-based hierarchical representation. lexicalized tree adjoining grammars have proved useful for nlp. however, numerous redundancy problems face ltags developers, as highlighted by vijay-shanker and schabes (92).we present and a tool that automatically generates the tree families of an ltag. it starts from a compact hierarchical organization of syntactic descriptions that is linguistically motivated and carries out all the relevant combinations of linguistic phenomena.
building parallel ltag for french and italian. in this paper we view lexicalized tree adjoining grammars as the compilation of a more abstract and modular layer of linguistic description: the metagrammar (mg). mg provides a hierarchical representation of lexico-syntactic descriptions and principles that capture the well-formedness of lexicalized structures, expressed using syntactic functions. this makes it possible for a tool to compile an instance of mg into an ltag, automatically performing the relevant combinations of linguistic phenomena. we then describe the instantiation of an mg for italian and french. the work for french was performed starting with an existing ltag, which has been augmented as a result. the work for italian was performed by systematic contrast with the french mg. the automatic compilation gives two parallel ltag, compatible for multilingual nlp applications.
uncertainty reduction in collaborative bootstrapping: measure and algorithm. this paper proposes the use of uncertainty reduction in machine learning methods such as co-training and bilingual boot-strapping, which are referred to, in a general term, as 'collaborative bootstrapping'. the paper indicates that uncertainty reduction is an important factor for enhancing the performance of collaborative bootstrapping. it proposes a new measure for representing the degree of uncertainty correlation of the two classifiers in collaborative bootstrapping and uses the measure in analysis of collaborative bootstrapping. furthermore, it proposes a new algorithm of collaborative bootstrapping on the basis of uncertainty reduction. experimental results have verified the correctness of the analysis and have demonstrated the significance of the new algorithm.
automatic construction of a hypernym-labeled noun hierarchy from text. previous work has shown that automatic methods can be used in building semantic lexicons. this work goes a step further by automatically creating not just clusters of related words, but a hierarchy of nouns and their hypernyms, akin to the hand-built hierarchy in wordnet.
a pragmatics-based approach to understanding intersentential ellipsis. intersentential elliptical utterances occur frequently in information-seeking dialogues. this paper presents a pragmatics-based framework for interpreting such utterances, including identification of the speaker's discourse goal in employing the fragment. we claim that the advantage of this approach is its reliance upon pragmatic information, including discourse content and conversational goals, rather than upon precise representations of the preceding utterance alone.
metapher - a key to extensible semantic analysis. interpreting metaphors is an integral and inescapable process in human understanding of natural language. this paper discusses a method of analyzing metaphors based on the existence of a small number of generalized metaphor mappings. each generalized metaphor contains a recognition network, a basic mapping, additional transfer mappings, and an implicit intention component. it is argued that the method reduces metaphor interpretation from a reconstruction to a recognition task. implications towards automating certain aspects of language learning are also discussed.
corpus-based acquisition of relative pronoun disambiguation heuristics. this paper presents a corpus-based approach for deriving heuristics to locate the antecedents of relative pronouns. the technique duplicates the performance of hand-coded rules and requires human intervention only during the training phase. because the training instances are built on parser output rather than word cooccurrences, the technique requires a small number of training examples and can be used on small to medium-sized corpora. our initial results suggest that the approach may provide a general method for the automated acquisition of a variety of disambiguation heuristics for natural language systems, especially for problems that require the assimilation of syntactic and semantic knowledge.
error-driven pruning of treebank grammars for base noun phrase identification. finding simple, non-recursive, base noun phrases is an important subtask for many natural language processing applications. while previous empirical methods for base np identification have been rather complex, this paper instead proposes a very simple algorithm that is tailored to the relative simplicity of the task. in particular, we present a corpus-based approach for finding base nps by matching part-of-speech tag sequences. the training phase of the algorithm is based on two successful techniques: first the base np grammar is read from a "treebank" corpus; then the grammar is improved by selecting rules with high "benefit" scores. using this simple algorithm with a naive heuristic for matching rules, we achieve surprising accuracy in an evaluation on the penn treebank wall street journal.
an empirical study of the influence of argument conciseness on argument effectiveness. we have developed a system that generates evaluative arguments that are tailored to the user, properly arranged and concise. we have also developed an evaluation framework in which the effectiveness of evaluative arguments can be measured with real users. this paper presents the results of a formal experiment we have performed in our framework to verify the influence of argument conciseness on argument effectiveness
paralanguage in computer mediated communication. this paper reports on some of the components of person to person communication mediated by computer conferenceing systems. transcripts from two systems were analysed: the electronic information and exchange system (eies), based at the new jersey institute of technology; and planet, based at infomedia inc. in palo alto, california. the research focused upon the ways in which expressive communication is encoded by users of the medium
inclusion, disjointness and choice: the logic of linguistic classification. we investigate the logical structure of concepts generated by conjunction and disjunction over a monotonic multiple inheritance network where concept nodes represent linguistic categories and links indicate basic inclusion (isa) and disjointness (isnota) relations. we model the distinction between primitive and defined concepts as well as between closed-and open-world reasoning. we apply our logical analysis to the sort inheritance and unification system of hpsg and also to classification in systemic choice systems.
word sense disambiguation vs. statistical machine translation. we directly investigate a subject of much recent debate: do word sense disambiguation models help statistical machine translation quality? we present empirical results casting doubt on this common, but unproved, assumption. using a state-of-the-art chinese word sense disambiguation model to choose translation candidates for a typical ibm statistical mt system, we find that word sense disambiguation does not yield significantly better translation quality than the statistical machine translation system alone. error analysis suggests several key factors behind this surprising finding, including inherent limitations of current statistical mt architectures.
relating complexity to practical performance in parsing with wide-coverage unification grammars. the paper demonstrates that exponential complexities with respect to grammar size and input length have little impact on the performance of three unification-based parsing algorithms, using a wide-coverage grammar. the results imply that the study and optimisation of unification-based parsing must rely on empirical data until complexity theory can more accurately predict the practical behaviour of such parsers1.
lattice-based word identification in clare. i argue that because of spelling and typing errors and other properties of typed text, the identification of words and word boundaries in general requires syntactic and semantic knowledge. a lattice representation is therefore appropriate for lexical analysis. i show how the use of such a representation in the clare system allows different kinds of hypothesis about word identity to be integrated in a uniform framework. i then describe a quantitative evaluation of clare's performance on a set of sentences into which typographic errors have been introduced. the results show that syntax and semantics can be applied as powerful sources of constraint on the possible corrections for misspelled words.
n semantic classes are harder than two. we show that we can automatically classify semantically related phrases into 10 classes. classification robustness is improved by training with multiple sources of evidence, including within-document cooccurrence, html markup, syntactic relationships in sentences, substitutability in query logs, and string similarity. our work provides a benchmark for automatic n-way classification into wordnet's semantic classes, both on a trec news corpus and on a corpus of substitutable search query phrases.
non-verbal cues for discourse structure. this paper addresses the issue of designing embodied conversational agents that exhibit appropriate posture shifts during dialogues with human users. previous research has noted the importance of hand gestures, eye gaze and head nods in conversations between embodied agents and humans. we present an analysis of human monologues and dialogues that suggests that postural shifts can be predicted as a function of discourse state in monologues, and discourse and conversation state in dialogues. on the basis of these findings, we have implemented an embodied conversational agent that uses collagen in such a way as to generate postural shifts.
computational lexical semantics, incrementality, and the so-called punctuality of events. the distinction between <i>achievements and accomplishments</i> is known to be an empirically important but subtle one. it is argued here to depend on the <i>atomicity</i> (rather than punctuality) of events, and to be strongly related to <i>incrementality</i> (i.e., to event-object mapping functions). a computational treatment of incrementality and atomicity is discussed in the paper, and a number or related empirical problems considered, notably lexical polysemy in verb-argument relationships.
optimization in multimodal interpretation. in a multimodal conversation, the way users communicate with a system depends on the available interaction channels and the situated context (e.g., conversation focus, visual feedback). these dependencies form a rich set of constraints from various perspectives such as temporal alignments between different modalities, coherence of conversation, and the domain semantics. there is strong evidence that competition and ranking of these constraints is important to achieve an optimal interpretation. thus, we have developed an optimization approach for multimodal interpretation, particularly for interpreting multimodal references. a preliminary evaluation indicates the effectiveness of this approach, especially for complex user inputs that involve multiple referring expressions in a speech utterance and multiple gestures.
towards conversational qa: automatic identification of problematic situations and user intent. to enable conversational qa, it is important to examine key issues addressed in conversational systems in the context of question answering. in conversational systems, understanding user intent is critical to the success of interaction. recent studies have also shown that the capability to automatically identify problematic situations during interaction can significantly improve the system performance. therefore, this paper investigates the new implications of user intent and problematic situations in the context of question answering. our studies indicate that, in basic interactive qa, there are different types of user intent that are tied to different kinds of system performance (e.g., problematic/error free situations). once users are motivated to find specific information related to their information goals, the interaction context can provide useful cues for the system to automatically identify problematic situations and user intent.
sense disambiguation using semantic relations and adjacency information. this paper describes a heuristic-based approach to word-sense disambiguation. the heuristics that are applied to disambiguate a word depend on its part of speech, and on its relationship to neighboring salient words in the text. parts of speech are found through a tagger, and related neighboring words are identified by a phrase extractor operating on the tagged text. to suggest possible senses, each heuristic draws on semantic relations extracted from a webster's dictionary and the semantic thesaurus wordnet. for a given word, all applicable heuristics are tried, and those senses that are rejected by all heuristics are discarded. in all, the disambiguator uses 39 heuristics based on 12 relationships.
estimating class priors in domain adaptation for word sense disambiguation. instances of a word drawn from different domains may have different sense priors (the proportions of the different senses of a word). this in turn affects the accuracy of word sense disambiguation (wsd) systems trained and applied on different domains. this paper presents a method to estimate the sense priors of words drawn from a new domain, and highlights the importance of using well calibrated probabilities when performing these estimations. by using well calibrated probabilities, we are able to estimate the sense priors effectively to achieve significant improvements in wsd accuracy.
an alignment method for noisy parallel corpora based on image processing techniques. this paper presents a new approach to bitext correspondence problem (bcp) of noisy bilingual corpora based on image processing (ip) techniques. by using one of several ways of estimating the lexical translation probability (ltp) between pairs of source and target words, we can turn a bitext into a discrete gray-level image. we contend that the bcp, when seen in the light, bears a striking resemblance to the line detection problem in ip. therefore, bcps, including sentence and word alignment, can benefit from a wealth of effective, well established ip techniques, including convolution-based filters, texture analysis and hough transform. this paper describes a new program, plotalign that produces a word-level bitext map for noisy or non-literal bitext, based on these techniques.
a pipeline framework for dependency parsing. pipeline computation, in which a task is decomposed into several stages that are solved sequentially, is a common computational strategy in natural language processing. the key problem of this model is that it results in error accumulation and suffers from its inability to correct mistakes in previous stages. we develop a framework for decisions made via in pipeline models, which addresses these difficulties, and presents and evaluates it in the context of bottom up dependency parsing for english. we show improvements in the accuracy of the inferred trees relative to existing models. interestingly, the proposed algorithm shines especially when evaluated globally, at a sentence level, where our results are significantly better than those of existing approaches.
gpsm: a generalized probabilistic semantic model for ambiguity resolution. in natural language processing, ambiguity resolution is a central issue, and can be regarded as a preference assignment problem. in this paper, a generalized probabilistic semantic model (gpsm) is proposed for preference computation. an effective semantic tagging procedure is proposed for tagging semantic features. a semantic score function is derived based on a score function, which integrates lexical, syntactic and semantic preference under a uniform formulation. the semantic score measure shows substantial improvement in structural disambiguation over a syntax-based approach.
immediate-head parsing for language models. we present two language models based upon an "immediate-head" parser --- our name for a parser that conditions all events below a constituent c upon the head of c. while all of the most accurate statistical parsers are of the immediate-head variety, no previous grammatical language model uses this technology. the perplexity for both of these models significantly improve upon the trigram model base-line as well as the best previous grammar-based language model. for the better of our two models these improvements are 24% and 14% respectively. we also suggest that improvement of the underlying parser should significantly improve the model's perplexity and that even in the near term there is a lot of potential for improvement in immediate-head language models.
a logic for semantic interpretation. we propose that logic (enhanced to encode probability information) is a good way of characterizing semantic interpretation. in support of this we give a fragment of an axiomatization for word-sense disambiguation, nounphrase (and verb) reference, and case disambiguation. we describe an inference engine (frail3) which actually takes this axiomatization and uses it to drive the semantic interpretation process. we claim three benefits from this scheme. first, the interface between semantic interpretation and pragmatics has always been problematic, since all of the above tasks in general require pragmatic inference. now the interface is trival, since both semantic interpretation and pragmatics use the same vocabulary and inference engine. the second benefit, related to the first, is that semantic guidance of syntax is a side effect of the interpretation. the third benefit is the elegance of the semantic interpretation theory. a few simple rules capture a remarkable diversity of semantic phenomena.
coarse-to-fine n-best parsing and maxent discriminative reranking. discriminative reranking is one method for constructing high-performance statistical parsers (collins, 2000). a discriminative reranker requires a source of candidate parses for each sentence. this paper describes a simple yet novel method for constructing sets of 50-best parses based on a coarse-to-fine generative parser (charniak, 2000). this method generates 50-best lists that are of substantially higher quality than previously obtainable. we used these parses as the input to a maxent reranker (johnson et al., 1999; riezler et al., 2002) that selects the best parse from the set of parses for each sentence, obtaining an f-score of 91.0% on sentences of length 100 or less.
word alignment in english-hindi parallel corpus using recency-vector approach: some studies. word alignment using recency-vector based approach has recently become popular. one major advantage of these techniques is that unlike other approaches they perform well even if the size of the parallel corpora is small. this makes these algorithms worth-studying for languages where resources are scarce. in this work we studied the performance of two very popular recency-vector based approaches, proposed in (fung and mckeown, 1994) and (somers, 1998), respectively, for word alignment in english-hindi parallel corpus. but performance of the above algorithms was not found to be satisfactory. however, subsequent addition of some new constraints improved the performance of the recency-vector based alignment technique significantly for the said corpus. the present paper discusses the new version of the algorithm and its performance in detail.
a hybrid convolution tree kernel for semantic role labeling. a hybrid convolution tree kernel is proposed in this paper to effectively model syntactic structures for semantic role labeling (srl). the hybrid kernel consists of two individual convolution kernels: a path kernel, which captures predicate-argument link features, and a constituent structure kernel, which captures the syntactic structure features of arguments. evaluation on the datasets of conll-2005 srl shared task shows that the novel hybrid convolution tree kernel out-performs the previous tree kernels. we also combine our new hybrid tree kernel based method with the standard rich flat feature based method. the experimental results show that the combinational method can get better performance than each of them individually.
a structured language model. the paper presents a language model that develops syntatic structure and uses it to extract meaningful information from the word history, thus enabling the use of long distance dependencies. the model assigns probability to every joint sequence of words-binary-parse-structure with headword annotation. the model, its probabilistic parametrization, and a set of experiments meant to evaluate its predictive power are presented.