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2502.14829
Measuring Faithfulness of Chains of Thought by Unlearning Reasoning Steps
cs.CL
When prompted to think step-by-step, language models (LMs) produce a chain of thought (CoT), a sequence of reasoning steps that the model supposedly used to produce its prediction. However, despite much work on CoT prompting, it is unclear if CoT reasoning is faithful to the models' parameteric beliefs. We introduce ...
2502.14830
Middle-Layer Representation Alignment for Cross-Lingual Transfer in Fine-Tuned LLMs
cs.CL cs.AI
While large language models demonstrate remarkable capabilities at task-specific applications through fine-tuning, extending these benefits across diverse languages is essential for broad accessibility. However, effective cross-lingual transfer is hindered by LLM performance gaps across languages and the scarcity of ...
2502.14831
Improving the Diffusability of Autoencoders
cs.CV cs.AI cs.LG
Latent diffusion models have emerged as the leading approach for generating high-quality images and videos, utilizing compressed latent representations to reduce the computational burden of the diffusion process. While recent advancements have primarily focused on scaling diffusion backbones and improving autoencoder...
2502.14833
Probabilistic Robustness in Deep Learning: A Concise yet Comprehensive Guide
cs.LG
Deep learning (DL) has demonstrated significant potential across various safety-critical applications, yet ensuring its robustness remains a key challenge. While adversarial robustness has been extensively studied in worst-case scenarios, probabilistic robustness (PR) offers a more practical perspective by quantifyin...
2502.14834
LongWriter-V: Enabling Ultra-Long and High-Fidelity Generation in Vision-Language Models
cs.CV cs.AI cs.CL
Existing Large Vision-Language Models (LVLMs) can process inputs with context lengths up to 128k visual and text tokens, yet they struggle to generate coherent outputs beyond 1,000 words. We find that the primary limitation is the absence of long output examples during supervised fine-tuning (SFT). To tackle this iss...
2502.14837
Towards Economical Inference: Enabling DeepSeek's Multi-Head Latent Attention in Any Transformer-based LLMs
cs.CL cs.AI
Multi-head Latent Attention (MLA) is an innovative architecture proposed by DeepSeek, designed to ensure efficient and economical inference by significantly compressing the Key-Value (KV) cache into a latent vector. Compared to MLA, standard LLMs employing Multi-Head Attention (MHA) and its variants such as Grouped-Q...
2502.14838
Revealing and Mitigating Over-Attention in Knowledge Editing
cs.CL cs.AI
Large Language Models have demonstrated superior performance across a wide range of tasks, but they still exhibit undesirable errors due to incorrect knowledge learned from the training data. To avoid this, knowledge editing methods emerged to precisely edit the specific model knowledge via efficiently modifying a ve...
2502.14840
Spatial Distribution-Shift Aware Knowledge-Guided Machine Learning
cs.LG
Given inputs of diverse soil characteristics and climate data gathered from various regions, we aimed to build a model to predict accurate land emissions. The problem is important since accurate quantification of the carbon cycle in agroecosystems is crucial for mitigating climate change and ensuring sustainable food...
2502.14842
Generating $\pi$-Functional Molecules Using STGG+ with Active Learning
cs.LG
Generating novel molecules with out-of-distribution properties is a major challenge in molecular discovery. While supervised learning methods generate high-quality molecules similar to those in a dataset, they struggle to generalize to out-of-distribution properties. Reinforcement learning can explore new chemical sp...
2502.14844
Dynamic Concepts Personalization from Single Videos
cs.GR cs.CV cs.LG
Personalizing generative text-to-image models has seen remarkable progress, but extending this personalization to text-to-video models presents unique challenges. Unlike static concepts, personalizing text-to-video models has the potential to capture dynamic concepts, i.e., entities defined not only by their appearan...
2502.14846
Scaling Text-Rich Image Understanding via Code-Guided Synthetic Multimodal Data Generation
cs.CV cs.CL
Reasoning about images with rich text, such as charts and documents, is a critical application of vision-language models (VLMs). However, VLMs often struggle in these domains due to the scarcity of diverse text-rich vision-language data. To address this challenge, we present CoSyn, a framework that leverages the codi...
2502.14848
GATE: Graph-based Adaptive Tool Evolution Across Diverse Tasks
cs.CL
Large Language Models (LLMs) have shown great promise in tool-making, yet existing frameworks often struggle to efficiently construct reliable toolsets and are limited to single-task settings. To address these challenges, we propose GATE (Graph-based Adaptive Tool Evolution), an adaptive framework that dynamically co...
2502.14853
On the $H$-property for Step-graphons: Residual Case
eess.SY cs.SY
We sample graphs $G_n$ on $n$ nodes from a step-graphon and evaluate the probability that $G_n$ has a Hamiltonian decomposition in the asymptotic regime as $n\to\infty$. It has recently been shown that for almost all step-graphons, this probability converges to either zero or one. In this paper, we focus on the class...
2502.14854
CLIPPER: Compression enables long-context synthetic data generation
cs.CL
LLM developers are increasingly reliant on synthetic data, but generating high-quality data for complex long-context reasoning tasks remains challenging. We introduce CLIPPER, a compression-based approach for generating synthetic data tailored to narrative claim verification - a task that requires reasoning over a bo...
2502.14855
Prompt-to-Leaderboard
cs.LG cs.CL
Large language model (LLM) evaluations typically rely on aggregated metrics like accuracy or human preference, averaging across users and prompts. This averaging obscures user- and prompt-specific variations in model performance. To address this, we propose Prompt-to-Leaderboard (P2L), a method that produces leaderbo...
2502.14856
FR-Spec: Accelerating Large-Vocabulary Language Models via Frequency-Ranked Speculative Sampling
cs.CL cs.AI cs.LG
Speculative sampling has emerged as an important technique for accelerating the auto-regressive generation process of large language models (LLMs) by utilizing a draft-then-verify mechanism to produce multiple tokens per forward pass. While state-of-the-art speculative sampling methods use only a single layer and a l...
2502.14860
Aligning LLMs to Ask Good Questions A Case Study in Clinical Reasoning
cs.CL
Large language models (LLMs) often fail to ask effective questions under uncertainty, making them unreliable in domains where proactive information-gathering is essential for decisionmaking. We present ALFA, a framework that improves LLM question-asking by (i) decomposing the notion of a "good" question into a set of...
2502.14862
Interpretable Text Embeddings and Text Similarity Explanation: A Primer
cs.CL cs.AI cs.IR
Text embeddings and text embedding models are a backbone of many AI and NLP systems, particularly those involving search. However, interpretability challenges persist, especially in explaining obtained similarity scores, which is crucial for applications requiring transparency. In this paper, we give a structured ove...
2502.14864
Benchmarking Multimodal RAG through a Chart-based Document Question-Answering Generation Framework
cs.AI cs.CV
Multimodal Retrieval-Augmented Generation (MRAG) enhances reasoning capabilities by integrating external knowledge. However, existing benchmarks primarily focus on simple image-text interactions, overlooking complex visual formats like charts that are prevalent in real-world applications. In this work, we introduce a...
2502.14865
Time Travel: A Comprehensive Benchmark to Evaluate LMMs on Historical and Cultural Artifacts
cs.CV cs.LG
Understanding historical and cultural artifacts demands human expertise and advanced computational techniques, yet the process remains complex and time-intensive. While large multimodal models offer promising support, their evaluation and improvement require a standardized benchmark. To address this, we introduce Tim...
2502.14866
LServe: Efficient Long-sequence LLM Serving with Unified Sparse Attention
cs.CL cs.AI cs.DC cs.LG cs.PF
Large language models (LLMs) have shown remarkable potential in processing long sequences, yet efficiently serving these long-context models remains challenging due to the quadratic computational complexity of attention in the prefilling stage and the large memory footprint of the KV cache in the decoding stage. To a...
adap-org/9807003
Development and Evolution of Neural Networks in an Artificial Chemistry
adap-org cs.NE nlin.AO q-bio.PE
We present a model of decentralized growth for Artificial Neural Networks (ANNs) inspired by the development and the physiology of real nervous systems. In this model, each individual artificial neuron is an autonomous unit whose behavior is determined only by the genetic information it harbors and local concentratio...
adap-org/9903003
Evolution of genetic organization in digital organisms
adap-org cs.NE nlin.AO q-bio.PE
We examine the evolution of expression patterns and the organization of genetic information in populations of self-replicating digital organisms. Seeding the experiments with a linearly expressed ancestor, we witness the development of complex, parallel secondary expression patterns. Using principles from information...
alg-geom/9608018
Rank Two Bundles on Algebraic Curves and Decoding of Goppa Codes
alg-geom cs.IT math.AG math.IT
We study a connection between two topics: Decoding of Goppa codes arising from an algebraic curve, and rank two extensions of certain line bundles on the curve.
astro-ph/0008307
Science User Scenarios for a Virtual Observatory Design Reference Mission: Science Requirements for Data Mining
astro-ph cs.DB cs.DL cs.IR
The knowledge discovery potential of the new large astronomical databases is vast. When these are used in conjunction with the rich legacy data archives, the opportunities for scientific discovery multiply rapidly. A Virtual Observatory (VO) framework will enable transparent and efficient access, search, retrieval, a...
astro-ph/0010583
Data Mining in Astronomical Databases
astro-ph cs.DB cs.DL cs.IR
A Virtual Observatory (VO) will enable transparent and efficient access, search, retrieval, and visualization of data across multiple data repositories, which are generally heterogeneous and distributed. Aspects of data mining that apply to a variety of science user scenarios with a VO are reviewed.
astro-ph/0402591
Evolutionary design of photometric systems and its application to Gaia
astro-ph cs.NE stat.ML
Designing a photometric system to best fulfil a set of scientific goals is a complex task, demanding a compromise between conflicting requirements and subject to various constraints. A specific example is the determination of stellar astrophysical parameters (APs) - effective temperature, metallicity etc. - across a ...
astro-ph/0502164
Particle Swarm Optimization: An efficient method for tracing periodic orbits in 3D galactic potentials
astro-ph cs.NA cs.NE math.NA nlin.CD
We propose the Particle Swarm Optimization (PSO) as an alternative method for locating periodic orbits in a three--dimensional (3D) model of barred galaxies. We develop an appropriate scheme that transforms the problem of finding periodic orbits into the problem of detecting global minimizers of a function, which is ...
astro-ph/0504006
Virtual Observatory: From Concept to Implementation
astro-ph cs.CE
We review the origins of the Virtual Observatory (VO) concept, and the current status of the efforts in this field. VO is the response of the astronomical community to the challenges posed by the modern massive and complex data sets. It is a framework in which information technology is harnessed to organize, maintain...
astro-ph/0506110
Galactic Gradients, Postbiological Evolution and the Apparent Failure of SETI
astro-ph cs.AI physics.soc-ph
Motivated by recent developments impacting our view of Fermi's paradox (absence of extraterrestrials and their manifestations from our past light cone), we suggest a reassessment of the problem itself, as well as of strategies employed by SETI projects so far. The need for such reevaluation is fueled not only by the ...
astro-ph/0506308
Fast directional continuous spherical wavelet transform algorithms
astro-ph cs.IT math.IT
We describe the construction of a spherical wavelet analysis through the inverse stereographic projection of the Euclidean planar wavelet framework, introduced originally by Antoine and Vandergheynst and developed further by Wiaux et al. Fast algorithms for performing the directional continuous wavelet analysis on th...
astro-ph/0605042
How accurate are the time delay estimates in gravitational lensing?
astro-ph cs.LG
We present a novel approach to estimate the time delay between light curves of multiple images in a gravitationally lensed system, based on Kernel methods in the context of machine learning. We perform various experiments with artificially generated irregularly-sampled data sets to study the effect of the various lev...
astro-ph/0609159
A directional continuous wavelet transform on the sphere
astro-ph cs.IT math.IT
A new construction of a directional continuous wavelet analysis on the sphere is derived herein. We adopt the harmonic scaling idea for the spherical dilation operator recently proposed by Sanz et al. but extend the analysis to a more general directional framework. Directional wavelets are a powerful extension that a...
astro-ph/0612688
Optimal filters on the sphere
astro-ph cs.IT math.IT
We derive optimal filters on the sphere in the context of detecting compact objects embedded in a stochastic background process. The matched filter and the scale adaptive filter are derived on the sphere in the most general setting, allowing for directional template profiles and filters. The performance and relative ...
cmp-lg/9404001
An Alternative Conception of Tree-Adjoining Derivation
cmp-lg cs.CL
The precise formulation of derivation for tree-adjoining grammars has important ramifications for a wide variety of uses of the formalism, from syntactic analysis to semantic interpretation and statistical language modeling. We argue that the definition of tree-adjoining derivation must be reformulated in order to ma...
cmp-lg/9404002
Lessons from a Restricted Turing Test
cmp-lg cs.CL
We report on the recent Loebner prize competition inspired by Turing's test of intelligent behavior. The presentation covers the structure of the competition and the outcome of its first instantiation in an actual event, and an analysis of the purpose, design, and appropriateness of such a competition. We argue that ...
cmp-lg/9404003
Restricting the Weak-Generative Capacity of Synchronous Tree-Adjoining Grammars
cmp-lg cs.CL
The formalism of synchronous tree-adjoining grammars, a variant of standard tree-adjoining grammars (TAG), was intended to allow the use of TAGs for language transduction in addition to language specification. In previous work, the definition of the transduction relation defined by a synchronous TAG was given by appe...
cmp-lg/9404004
An Empirically Motivated Reinterpretation of Dependency Grammar
cmp-lg cs.CL
Dependency grammar is usually interpreted as equivalent to a strict form of X--bar theory that forbids the stacking of nodes of the same bar level (e.g., N' immediately dominating N' with the same head). But adequate accounts of _one_--anaphora and of the semantics of multiple modifiers require such stacking and acco...
cmp-lg/9404005
Memoization in Constraint Logic Programming
cmp-lg cs.CL
This paper shows how to apply memoization (caching of subgoals and associated answer substitutions) in a constraint logic programming setting. The research is is motivated by the desire to apply constraint logic programming (CLP) to problems in natural language processing that involve (constraint) interleaving or cor...
cmp-lg/9404006
SPANISH 1992 (S92): corpus-based analysis of present-day Spanish for medical purposes
cmp-lg cs.CL
S92 research was begun in 1987 to analyze word frequencies in present-day Spanish for making speech pathology evaluation tools. 500 2,000-word samples of children, adolescents and adults' language were input between 1988-1991, calculations done in 1992; statistical and Lewandowski analyses were carried out in 1993.
cmp-lg/9404007
Constraint-Based Categorial Grammar
cmp-lg cs.CL
We propose a generalization of Categorial Grammar in which lexical categories are defined by means of recursive constraints. In particular, the introduction of relational constraints allows one to capture the effects of (recursive) lexical rules in a computationally attractive manner. We illustrate the linguistic mer...
cmp-lg/9404008
Principles and Implementation of Deductive Parsing
cmp-lg cs.CL
We present a system for generating parsers based directly on the metaphor of parsing as deduction. Parsing algorithms can be represented directly as deduction systems, and a single deduction engine can interpret such deduction systems so as to implement the corresponding parser. The method generalizes easily to parse...
cmp-lg/9404009
A Deductive Account of Quantification in LFG
cmp-lg cs.CL
The relationship between Lexical-Functional Grammar (LFG) functional structures (f-structures) for sentences and their semantic interpretations can be expressed directly in a fragment of linear logic in a way that explains correctly the constrained interactions between quantifier scope ambiguity and bound anaphora. T...
cmp-lg/9404010
Intensional Verbs Without Type-Raising or Lexical Ambiguity
cmp-lg cs.CL
We present an analysis of the semantic interpretation of intensional verbs such as seek that allows them to take direct objects of either individual or quantifier type, producing both de dicto and de re readings in the quantifier case, all without needing to stipulate type-raising or quantifying-in rules. This simple...
cmp-lg/9404011
Adjuncts and the Processing of Lexical Rules
cmp-lg cs.CL
The standard HPSG analysis of Germanic verb clusters can not explain the observed narrow-scope readings of adjuncts in such verb clusters. We present an extension of the HPSG analysis that accounts for the systematic ambiguity of the scope of adjuncts in verb cluster constructions, by treating adjuncts as members of ...
cmp-lg/9405001
Similarity-Based Estimation of Word Cooccurrence Probabilities
cmp-lg cs.CL
In many applications of natural language processing it is necessary to determine the likelihood of a given word combination. For example, a speech recognizer may need to determine which of the two word combinations ``eat a peach'' and ``eat a beach'' is more likely. Statistical NLP methods determine the likelihood of...
cmp-lg/9405002
Temporal Relations: Reference or Discourse Coherence?
cmp-lg cs.CL
The temporal relations that hold between events described by successive utterances are often left implicit or underspecified. We address the role of two phenomena with respect to the recovery of these relations: (1) the referential properties of tense, and (2) the role of temporal constraints imposed by coherence rel...
cmp-lg/9405003
Some Bibliographical References on Intonation and Intonational Meaning
cmp-lg cs.CL
A by-no-means-complete collection of references for those interested in intonational meaning, with other miscellaneous references on intonation included. Additional references are welcome, and should be sent to julia@research.att.com.
cmp-lg/9405004
Syntactic-Head-Driven Generation
cmp-lg cs.CL
The previously proposed semantic-head-driven generation methods run into problems if none of the daughter constituents in the syntacto-semantic rule schemata of a grammar fits the definition of a semantic head given in Shieber et al. 1990. This is the case for the semantic analysis rules of certain constraint-based s...
cmp-lg/9405005
Pearl: A Probabilistic Chart Parser
cmp-lg cs.CL
This paper describes a natural language parsing algorithm for unrestricted text which uses a probability-based scoring function to select the "best" parse of a sentence. The parser, Pearl, is a time-asynchronous bottom-up chart parser with Earley-type top-down prediction which pursues the highest-scoring theory in th...
cmp-lg/9405006
Efficiency, Robustness, and Accuracy in Picky Chart Parsing
cmp-lg cs.CL
This paper describes Picky, a probabilistic agenda-based chart parsing algorithm which uses a technique called {\em probabilistic prediction} to predict which grammar rules are likely to lead to an acceptable parse of the input. Using a suboptimal search method, Picky significantly reduces the number of edges produce...
cmp-lg/9405007
Towards History-based Grammars: Using Richer Models for Probabilistic Parsing
cmp-lg cs.CL
We describe a generative probabilistic model of natural language, which we call HBG, that takes advantage of detailed linguistic information to resolve ambiguity. HBG incorporates lexical, syntactic, semantic, and structural information from the parse tree into the disambiguation process in a novel way. We use a corp...
cmp-lg/9405008
A Stochastic Finite-State Word-Segmentation Algorithm for Chinese
cmp-lg cs.CL
We present a stochastic finite-state model for segmenting Chinese text into dictionary entries and productively derived words, and providing pronunciations for these words; the method incorporates a class-based model in its treatment of personal names. We also evaluate the system's performance, taking into account th...
cmp-lg/9405009
Natural Language Parsing as Statistical Pattern Recognition
cmp-lg cs.CL
Traditional natural language parsers are based on rewrite rule systems developed in an arduous, time-consuming manner by grammarians. A majority of the grammarian's efforts are devoted to the disambiguation process, first hypothesizing rules which dictate constituent categories and relationships among words in ambigu...
cmp-lg/9405010
Common Topics and Coherent Situations: Interpreting Ellipsis in the Context of Discourse Inference
cmp-lg cs.CL
It is claimed that a variety of facts concerning ellipsis, event reference, and interclausal coherence can be explained by two features of the linguistic form in question: (1) whether the form leaves behind an empty constituent in the syntax, and (2) whether the form is anaphoric in the semantics. It is proposed that...
cmp-lg/9405011
A Plan-Based Model for Response Generation in Collaborative Task-Oriented Dialogues
cmp-lg cs.CL
This paper presents a plan-based architecture for response generation in collaborative consultation dialogues, with emphasis on cases in which the system (consultant) and user (executing agent) disagree. Our work contributes to an overall system for collaborative problem-solving by providing a plan-based framework th...
cmp-lg/9405012
Integration Of Visual Inter-word Constraints And Linguistic Knowledge In Degraded Text Recognition
cmp-lg cs.CL
Degraded text recognition is a difficult task. Given a noisy text image, a word recognizer can be applied to generate several candidates for each word image. High-level knowledge sources can then be used to select a decision from the candidate set for each word image. In this paper, we propose that visual inter-word ...
cmp-lg/9405013
Collaboration on reference to objects that are not mutually known
cmp-lg cs.CL
In conversation, a person sometimes has to refer to an object that is not previously known to the other participant. We present a plan-based model of how agents collaborate on reference of this sort. In making a reference, an agent uses the most salient attributes of the referent. In understanding a reference, an age...
cmp-lg/9405014
Classifying Cue Phrases in Text and Speech Using Machine Learning
cmp-lg cs.CL
Cue phrases may be used in a discourse sense to explicitly signal discourse structure, but also in a sentential sense to convey semantic rather than structural information. This paper explores the use of machine learning for classifying cue phrases as discourse or sentential. Two machine learning programs (Cgrendel a...
cmp-lg/9405015
Intention-based Segmentation: Human Reliability and Correlation with Linguistic Cues
cmp-lg cs.CL
Certain spans of utterances in a discourse, referred to here as segments, are widely assumed to form coherent units. Further, the segmental structure of discourse has been claimed to constrain and be constrained by many phenomena. However, there is weak consensus on the nature of segments and the criteria for recogni...
cmp-lg/9405016
Precise n-gram Probabilities from Stochastic Context-free Grammars
cmp-lg cs.CL
We present an algorithm for computing n-gram probabilities from stochastic context-free grammars, a procedure that can alleviate some of the standard problems associated with n-grams (estimation from sparse data, lack of linguistic structure, among others). The method operates via the computation of substring expecta...
cmp-lg/9405017
Best-first Model Merging for Hidden Markov Model Induction
cmp-lg cs.CL
This report describes a new technique for inducing the structure of Hidden Markov Models from data which is based on the general `model merging' strategy (Omohundro 1992). The process begins with a maximum likelihood HMM that directly encodes the training data. Successively more general models are produced by merging...
cmp-lg/9405018
Memory-Based Lexical Acquisition and Processing
cmp-lg cs.CL
Current approaches to computational lexicology in language technology are knowledge-based (competence-oriented) and try to abstract away from specific formalisms, domains, and applications. This results in severe complexity, acquisition and reusability bottlenecks. As an alternative, we propose a particular performan...
cmp-lg/9405019
Determination of referential property and number of nouns in Japanese sentences for machine translation into English
cmp-lg cs.CL
When translating Japanese nouns into English, we face the problem of articles and numbers which the Japanese language does not have, but which are necessary for the English composition. To solve this difficult problem we classified the referential property and the number of nouns into three types respectively. This p...
cmp-lg/9405020
Capturing CFLs with Tree Adjoining Grammars
cmp-lg cs.CL
We define a decidable class of TAGs that is strongly equivalent to CFGs and is cubic-time parsable. This class serves to lexicalize CFGs in the same manner as the LCFGs of Schabes and Waters but with considerably less restriction on the form of the grammars. The class provides a normal form for TAGs that generate loc...
cmp-lg/9405021
Generating Precondition Expressions in Instructional Text
cmp-lg cs.CL
This study employs a knowledge intensive corpus analysis to identify the elements of the communicative context which can be used to determine the appropriate lexical and grammatical form of instructional texts. \ig, an instructional text generation system based on this analysis, is presented, particularly with refere...
cmp-lg/9405022
Grammar Specialization through Entropy Thresholds
cmp-lg cs.CL
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 (o...
cmp-lg/9405023
An Integrated Heuristic Scheme for Partial Parse Evaluation
cmp-lg cs.CL
GLR* is a recently developed robust version of the Generalized LR Parser, that can parse almost ANY input sentence by ignoring unrecognizable parts of the sentence. On a given input sentence, the parser returns a collection of parses that correspond to maximal, or close to maximal, parsable subsets of the original in...
cmp-lg/9405024
Abductive Equivalential Translation and its application to Natural Language Database Interfacing
cmp-lg cs.CL
The thesis describes a logical formalization of natural-language database interfacing. We assume the existence of a ``natural language engine'' capable of mediating between surface linguistic string and their representations as ``literal'' logical forms: the focus of interest will be the question of relating ``litera...
cmp-lg/9405025
An Optimal Tabular Parsing Algorithm
cmp-lg cs.CL
In this paper we relate a number of parsing algorithms which have been developed in very different areas of parsing theory, and which include deterministic algorithms, tabular algorithms, and a parallel algorithm. We show that these algorithms are based on the same underlying ideas. By relating existing ideas, we hop...
cmp-lg/9405026
An Extended Theory of Head-Driven Parsing
cmp-lg cs.CL
We show that more head-driven parsing algorithms can be formulated than those occurring in the existing literature. These algorithms are inspired by a family of left-to-right parsing algorithms from a recent publication. We further introduce a more advanced notion of ``head-driven parsing'' which allows more detailed...
cmp-lg/9405027
Acquiring Receptive Morphology: A Connectionist Model
cmp-lg cs.CL
This paper describes a modular connectionist model of the acquisition of receptive inflectional morphology. The model takes inputs in the form of phones one at a time and outputs the associated roots and inflections. Simulations using artificial language stimuli demonstrate the capacity of the model to learn suffixat...
cmp-lg/9405028
Semantics of Complex Sentences in Japanese
cmp-lg cs.CL
The important part of semantics of complex sentence is captured as relations among semantic roles in subordinate and main clause respectively. However if there can be relations between every pair of semantic roles, the amount of computation to identify the relations that hold in the given sentence is extremely large....
cmp-lg/9405029
Structural Tags, Annealing and Automatic Word Classification
cmp-lg cs.CL
This paper describes an automatic word classification system which uses a locally optimal annealing algorithm and average class mutual information. A new word-class representation, the structural tag is introduced and its advantages for use in statistical language modelling are presented. A summary of some results wi...
cmp-lg/9405030
Priority Union and Generalization in Discourse Grammars
cmp-lg cs.CL
We describe an implementation in Carpenter's typed feature formalism, ALE, of a discourse grammar of the kind proposed by Scha, Polanyi, et al. We examine their method for resolving parallelism-dependent anaphora and show that there is a coherent feature-structural rendition of this type of grammar which uses the ope...
cmp-lg/9405031
An Attributive Logic of Set Descriptions and Set Operations
cmp-lg cs.CL
This paper provides a model theoretic semantics to feature terms augmented with set descriptions. We provide constraints to specify HPSG style set descriptions, fixed cardinality set descriptions, set-membership constraints, restricted universal role quantifications, set union, intersection, subset and disjointness. ...
cmp-lg/9405032
Modularity in a Connectionist Model of Morphology Acquisition
cmp-lg cs.CL
This paper describes a modular connectionist model of the acquisition of receptive inflectional morphology. The model takes inputs in the form of phones one at a time and outputs the associated roots and inflections. In its simplest version, the network consists of separate simple recurrent subnetworks for root and i...
cmp-lg/9405033
Relating Complexity to Practical Performance in Parsing with Wide-Coverage Unification Grammars
cmp-lg cs.CL
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 d...
cmp-lg/9405034
Extracting Noun Phrases from Large-Scale Texts: A Hybrid Approach and Its Automatic Evaluation
cmp-lg cs.CL
To acquire noun phrases from running texts is useful for many applications, such as word grouping,terminology indexing, etc. The reported literatures adopt pure probabilistic approach, or pure rule-based noun phrases grammar to tackle this problem. In this paper, we apply a probabilistic chunker to deciding the impli...
cmp-lg/9405035
Dual-Coding Theory and Connectionist Lexical Selection
cmp-lg cs.CL
We introduce the bilingual dual-coding theory as a model for bilingual mental representation. Based on this model, lexical selection neural networks are implemented for a connectionist transfer project in machine translation. This lexical selection approach has two advantages. First, it is learnable. Little human eff...
cmp-lg/9406001
Intentions and Information in Discourse
cmp-lg cs.CL
This paper is about the flow of inference between communicative intentions, discourse structure and the domain during discourse processing. We augment a theory of discourse interpretation with a theory of distinct mental attitudes and reasoning about them, in order to provide an account of how the attitudes interact ...
cmp-lg/9406002
Speech Dialogue with Facial Displays: Multimodal Human-Computer Conversation
cmp-lg cs.CL
Human face-to-face conversation is an ideal model for human-computer dialogue. One of the major features of face-to-face communication is its multiplicity of communication channels that act on multiple modalities. To realize a natural multimodal dialogue, it is necessary to study how humans perceive information and d...
cmp-lg/9406003
A Learning Approach to Natural Language Understanding
cmp-lg cs.CL
In this paper we propose a learning paradigm for the problem of understanding spoken language. The basis of the work is in a formalization of the understanding problem as a communication problem. This results in the definition of a stochastic model of the production of speech or text starting from the meaning of a se...
cmp-lg/9406004
Towards a Principled Representation of Discourse Plans
cmp-lg cs.CL
We argue that discourse plans must capture the intended causal and decompositional relations between communicative actions. We present a planning algorithm, DPOCL, that builds plan structures that properly capture these relations, and show how these structures are used to solve the problems that plagued previous disc...
cmp-lg/9406005
Word-Sense Disambiguation Using Decomposable Models
cmp-lg cs.CL
Most probabilistic classifiers used for word-sense disambiguation have either been based on only one contextual feature or have used a model that is simply assumed to characterize the interdependencies among multiple contextual features. In this paper, a different approach to formulating a probabilistic model is pres...
cmp-lg/9406006
Detecting and Correcting Speech Repairs
cmp-lg cs.CL
Interactive spoken dialog provides many new challenges for spoken language systems. One of the most critical is the prevalence of speech repairs. This paper presents an algorithm that detects and corrects speech repairs based on finding the repair pattern. The repair pattern is built by finding word matches and word ...
cmp-lg/9406007
Aligning a Parallel English-Chinese Corpus Statistically with Lexical Criteria
cmp-lg cs.CL
We describe our experience with automatic alignment of sentences in parallel English-Chinese texts. Our report concerns three related topics: (1) progress on the HKUST English-Chinese Parallel Bilingual Corpus; (2) experiments addressing the applicability of Gale & Church's length-based statistical method to the ...
cmp-lg/9406008
Parsing Turkish with the Lexical Functional Grammar Formalism
cmp-lg cs.CL
This paper describes our work on parsing Turkish using the lexical-functional grammar formalism. This work represents the first significant effort for parsing Turkish. Our implementation is based on Tomita's parser developed at Carnegie-Mellon University Center for Machine Translation. The grammar covers a substantia...
cmp-lg/9406009
Multiset-Valued Linear Index Grammars: Imposing Dominance Constraints on Derivations
cmp-lg cs.CL
This paper defines multiset-valued linear index grammar and unordered vector grammar with dominance links. The former models certain uses of multiset-valued feature structures in unification-based formalisms, while the latter is motivated by word order variation and by ``quasi-trees'', a generalization of trees. The ...
cmp-lg/9406010
Some Advances in Transformation-Based Part of Speech Tagging
cmp-lg cs.CL
Most recent research in trainable part of speech taggers has explored stochastic tagging. While these taggers obtain high accuracy, linguistic information is captured indirectly, typically in tens of thousands of lexical and contextual probabilities. In [Brill92], a trainable rule-based tagger was described that obta...
cmp-lg/9406011
Exploring the Statistical Derivation of Transformational Rule Sequences for Part-of-Speech Tagging
cmp-lg cs.CL
Eric Brill has recently proposed a simple and powerful corpus-based language modeling approach that can be applied to various tasks including part-of-speech tagging and building phrase structure trees. The method learns a series of symbolic transformational rules, which can then be applied in sequence to a test corpu...
cmp-lg/9406012
Self-Organizing Machine Translation: Example-Driven Induction of Transfer Functions
cmp-lg cs.CL
With the advent of faster computers, the notion of doing machine translation from a huge stored database of translation examples is no longer unreasonable. This paper describes an attempt to merge the Example-Based Machine Translation (EBMT) approach with psycholinguistic principles. A new formalism for context- free...
cmp-lg/9406013
Graded Unification: A Framework for Interactive Processing
cmp-lg cs.CL
An extension to classical unification, called {\em graded unification} is presented. It is capable of combining contradictory information. An interactive processing paradigm and parser based on this new operator are also presented.
cmp-lg/9406014
A Hybrid Reasoning Model for Indirect Answers
cmp-lg cs.CL
This paper presents our implemented computational model for interpreting and generating indirect answers to Yes-No questions. Its main features are 1) a discourse-plan-based approach to implicature, 2) a reversible architecture for generation and interpretation, 3) a hybrid reasoning model that employs both plan infe...
cmp-lg/9406015
Statistical Augmentation of a Chinese Machine-Readable Dictionary
cmp-lg cs.CL
We describe a method of using statistically-collected Chinese character groups from a corpus to augment a Chinese dictionary. The method is particularly useful for extracting domain-specific and regional words not readily available in machine-readable dictionaries. Output was evaluated both using human evaluators and...
cmp-lg/9406016
Corpus-Driven Knowledge Acquisition for Discourse Analysis
cmp-lg cs.CL
The availability of large on-line text corpora provides a natural and promising bridge between the worlds of natural language processing (NLP) and machine learning (ML). In recent years, the NLP community has been aggressively investigating statistical techniques to drive part-of-speech taggers, but application-speci...
cmp-lg/9406017
An Automatic Method of Finding Topic Boundaries
cmp-lg cs.CL
This article outlines a new method of locating discourse boundaries based on lexical cohesion and a graphical technique called dotplotting. The application of dotplotting to discourse segmentation can be performed either manually, by examining a graph, or automatically, using an optimization algorithm. The results of...
cmp-lg/9406018
TDL--- A Type Description Language for Constraint-Based Grammars
cmp-lg cs.CL
This paper presents \tdl, a typed feature-based representation language and inference system. Type definitions in \tdl\ consist of type and feature constraints over the boolean connectives. \tdl\ supports open- and closed-world reasoning over types and allows for partitions and incompatible types. Working with partia...
cmp-lg/9406019
A Complete and Recursive Feature Theory
cmp-lg cs.CL
Various feature descriptions are being employed in logic programming languages and constrained-based grammar formalisms. The common notational primitive of these descriptions are functional attributes called features. The descriptions considered in this paper are the possibly quantified first-order formulae obtained ...
cmp-lg/9406020
DPOCL: A Principled Approach to Discourse Planning
cmp-lg cs.CL
Research in discourse processing has identified two representational requirements for discourse planning systems. First, discourse plans must adequately represent the intentional structure of the utterances they produce in order to enable a computational discourse agent to respond effectively to communicative failure...