August 07, 2009

ACL 2009 Notes

Some notable ACL talks with links and notes...
  • Tutorial: Kevin Knight, Philipp Koehn.
    Topics in Statistical Machine Translation
    MT: Phrase based, hierarchical, and syntax based approaches. Hiero is equivalent? to a syntax based approach with a single nonterminal. Minimum Bayes Risk (MBR) chooses not the best option but the one that has maximum expected BLEU. Approaches that work with lattices and forests. System combination provides significant gain. Integrating LM into decoding improves. Cube pruning makes hiero and syntax based more efficient. Throwing out 99% of the phrase table gives no loss. Factored models help when factors used as back off. Reordering tried before. The source and target can be string, tree, or forest. Arabic and Chinese seem most popular. Good test: can you put a jumbled up sentence in the right order. If we could output only grammatical sentences (simplified English?). Dependency LM for output language. Lattice translation. Giza alignments not very accurate, guess = 80%. Bleu between human translators is at the level of best systems, i.e. cannot use as upper bound.

  • Tutorial: Simone Paolo Ponzetto and Massimo Poesio.
    State-of-the-art NLP Approaches to Coreference Resolution: Theory and Practical Recipes
    Coref: ACE is current standard dataset. Also MUC and other new ones. Anaphora approx 50% proper nouns, 40% noun phrases, 10% pronouns. NPs most difficult. Tough to know when discourse-new. Have evaluation problems like other fields. Would think deciding on anaphora easier for annotators, but issues like whether to consider China and its population anaphoric.

  • P09-1001 [bib]: Qiang Yang; Yuqiang Chen; Gui-Rong Xue; Wenyuan Dai; Yong Yu.
    Heterogeneous Transfer Learning for Image Clustering via the SocialWeb
    ML: Qiang Yang gave the first invited talk. When the training and test sets have different distributions or different representations. Did not talk much about: when train and test have different labels. Link to causality. Unsupervised pre-learning boosting supervised learning curve.

  • P09-1002 [bib]: Katrin Erk; Diana McCarthy; Nicholas Gaylord.
    Investigations on Word Senses and Word Usages
    WSD: Annotators provide scores 1-5 for two tasks: how good a fit between a usage and sense, how close are two usages of same word. Claim forcing annotators to single decision detrimental. Also claim coarse senses insufficient to explain results.

  • P09-1010 [bib]: S.R.K. Branavan; Harr Chen; Luke Zettlemoyer; Regina Barzilay.
    Reinforcement Learning for Mapping Instructions to Actions
    Situated language: Best paper award. Good work goes beyond studying language in isolation. Reinforcement results sound incredibly good, number of features pretty small, how much prior info did they exactly use?

  • P09-1011 [bib]: Percy Liang; Michael Jordan; Dan Klein
    Learning Semantic Correspondences with Less Supervision
    Semantic representations: Learn semantic mappings in the domains of weather, robocup sportscasting, and NFL recaps when it is not clear what record and what field the text is referring to.

  • P09-1009 [bib]: Benjamin Snyder; Tahira Naseem; Regina Barzilay
    Unsupervised Multilingual Grammar Induction
    Syntax: A candidate constituent in one language may be split in another preventing wrong rules to be learnt.

  • P09-1024 [bib]: Christina Sauper; Regina Barzilay
    Automatically Generating Wikipedia Articles: A Structure-Aware Approach
    Summarization: I did not know summarization consists of cutting and pasting existing text.

  • P09-1025 [bib]: Neil McIntyre; Mirella Lapata
    Learning to Tell Tales: A Data-driven Approach to Story Generation
    Schemas: Learning a model of fairy tales to generate new ones. Nice idea but resulting stories not so good. Better models possible.

  • P09-1034 [bib]: Sebastian Pado; Michel Galley; Dan Jurafsky; Christopher D. Manning
    Robust Machine Translation Evaluation with Entailment Features
    MT: Compared to human judgement Meteor does best (significantly better than Bleu) among shallow evaluation metrics. Using RTE to see if the produced translation is an entailment or paraphrase of the reference does better.

  • P09-1039 [bib]: Andre Martins; Noah Smith; Eric Xing
    Concise Integer Linear Programming Formulations for Dependency Parsing
    Syntax: Best paper award.

  • P09-1040 [bib]: Joakim Nivre
    Non-Projective Dependency Parsing in Expected Linear Time
    Syntax: By adding one more operation that swaps tokens to the shift reduce parser, generation of nonprojective parses possible.

  • P09-1041 [bib]: Gregory Druck; Gideon Mann; Andrew McCallum
    Semi-supervised Learning of Dependency Parsers using Generalized Expectation Criteria
    Syntax: Instead of labeled data, use expectation constraints in training parser.

  • P09-1042 [bib]: Kuzman Ganchev; Jennifer Gillenwater; Ben Taskar
    Dependency Grammar Induction via Bitext Projection Constraints
    Syntax: Similar to above, but uses bitext constraints.

  • P09-1057 [bib]: Sujith Ravi; Kevin Knight
    Minimized Models for Unsupervised Part-of-Speech Tagging
    Syntax: Best paper award.

  • P09-1068 [bib]: Nathanael Chambers; Dan Jurafsky
    Unsupervised Learning of Narrative Schemas and their Participants
    Schemas: very nice work modeling structure of NYT stories. Could be improved by focusing on a particular genre and introducing narrative ordering to model (apparently time ordering is really difficult).

  • P09-1070 [bib]: Joseph Reisinger; Marius Pasca
    Latent Variable Models of Concept-Attribute Attachment
    SemRel: unsupervised learning of concept clusters and attributes for each cluster from text.

  • P09-1072 [bib]: Kai-min K. Chang; Vladimir L. Cherkassky; Tom M. Mitchell; Marcel Adam Just
    Quantitative modeling of the neural representation of adjective-noun phrases to account for fMRI activation
    Brain: continuing the work of brain imaging. Some success in guessing which adj-noun pair being thought. Better questions can be asked.

  • P09-2062 [bib]: Chris Biemann; Monojit Choudhury; Animesh Mukherjee
    Syntax is from Mars while Semantics from Venus! Insights from Spectral Analysis of Distributional Similarity Networks
    WSD: Qualitative differences between distributional similarity networks for semantics and syntax. Does it say anything about word meaning representation?

  • P09-2059 [bib]: Gumwon Hong; Seung-Wook Lee; Hae-Chang Rim
    Bridging Morpho-Syntactic Gap between Source and Target Sentences for English-Korean Statistical Machine Translation
    MT: Problems similar to Turkish. Collins '05 proposed reordering. Lee 06 removed useless function words. Hong inserts pseudo-words to xlate to Korean morphemes.

  • P09-2069 [bib]: Haşim Sak; Tunga Güngör; Murat Saraçlar
    A Stochastic Finite-State Morphological Parser for Turkish
    Mor: A probabilistic generative model for Turkish words.

  • P09-1076 [bib]: Bonnie Webber
    Genre distinctions for discourse in the Penn TreeBank
    Invited talk - Discourse: topics seem relevant to Schema learning, should find a good tutorial.

  • P09-1087 [bib]: Michel Galley; Christopher D. Manning
    Quadratic-Time Dependency Parsing for Machine Translation
    Syntax: nonprojective parsing tying each word to its most likely head. Why did this not work when I tried it in CoNLL? Gives O(n2). Could you adopt Nivre for linear? Unsupervised parsing? Using dependency LM as a feature.

  • P09-1088 [bib]: Phil Blunsom; Trevor Cohn; Chris Dyer; Miles Osborne
    A Gibbs Sampler for Phrasal Synchronous Grammar Induction
    MT: Bayesian magic. Look into SCFGs. Generates its own word alignment. Works better on non-monotonic language pairs, monotonic ones difficult to improve on.

  • P09-1089 [bib]: Shachar Mirkin; Lucia Specia; Nicola Cancedda; Ido Dagan; Marc Dymetman; Idan Szpektor
    Source-Language Entailment Modeling for Translating Unknown Terms
    MT: Generate paraphrases or entailments for unknown words using RTE.

  • P09-1090 [bib]: Ananthakrishnan Ramanathan; Hansraj Choudhary; Avishek Ghosh; Pushpak Bhattacharyya
    Case markers and Morphology: Addressing the crux of the fluency problem in English-Hindi SMT
    MT: Reordering and factored model. Fluency and adequacy manually evaluated in addition to BLEU.

  • P09-1108 [bib]: Adam Pauls; Dan Klein
    K-Best A* Parsing
    Syntax: Best paper award.

  • P09-1104 [bib]: Aria Haghighi; John Blitzer; John DeNero; Dan Klein
    Better Word Alignments with Supervised ITG Models
    MT: Check if they have code available. Claim 1.1 bleu improvement.

  • P09-1105 [bib]: Fei Huang
    Confidence Measure for Word Alignment
    MT: Measure confidence based on posterior probability, improve alignments.

  • P09-1113 [bib]: Mike Mintz; Steven Bills; Rion Snow; Daniel Jurafsky
    Distant supervision for relation extraction without labeled data
    SemRel: Unsupervised method.

  • P09-1116 [bib]: Dekang Lin; Xiaoyun Wu
    Phrase Clustering for Discriminative Learning
    WSD: cluster phrases instead of words. Much less ambiguous, so pure context. Use different size clusters together, let the learning algorithm pick. Similar to hierarchical. Improves NER and query classification. Any application where clustering words useful because of sparsity. Clusters derived from 700B web data. Are the clusters available?

  • P09-1117 [bib]: Katrin Tomanek; Udo Hahn
    Semi-Supervised Active Learning for Sequence Labeling
    ML: Self learning does not work because the instances with most confidence are not the useful ones. Active learning asks for labels of instances with least confidence. Boosting effect?

  • D09-1030 [bib]: Chris Callison-Burch
    Fast, Cheap, and Creative: Evaluating Translation Quality Using Amazon’s Mechanical Turk
    MT: This article has one answer to the BLEU upper bound question among other things. The following graph shows that professional humans still get higher Bleu compared to SMT systems (although this is using 10 reference translations). They mention Google MT got higher Bleu but probably the test set was used in training. Still gives relative performances. Also, amazing things apparently can be done with Amazon Turk. Should use them to judge Turkish alignment quality.

  • D09-1045 [bib]: Jeff Mitchell; Mirella Lapata
    Language Models Based on Semantic Composition
    LM: Using simple VSM model for semantics small improvement over trigrams.

  • W09-2504 [bib]: Idan Szpektor; Ido Dagan
    Augmenting WordNet-based Inference with Argument Mapping
    RTE: Some lexical substitutions require other words to be shuffled. Automatic learning of shuffling rules using DIRT.

  • W09-2506 [bib]: Stefan Thater; Georgiana Dinu; Manfred Pinkal
    Ranking Paraphrases in Context
    WSD: Using lexsub dataset. No dictionary (I think). VSM semantic representation. Check Mitchell&Lapata, Erk&Pado for prior work.

  • W09-2507 [bib]: Kirk Roberts
    Building an Annotated Textual Inference Corpus for Motion and Space

  • W09-2510 [bib]: David Clausen; Christopher D. Manning
    Presupposed Content and Entailments in Natural Language Inference
    RTE: Example: "Mary lied about buying a car" -> Mary did not buy a car. "Mary regretted buying a car" -> Mary bought a car. "Mary thought about buying a car" -> Uncertain. Kartunnen 1975 presupposition projection. Check out NatLog system (natural logic).

  • D09-1058 [bib]: Jun Suzuki; Hideki Isozaki; Xavier Carreras; Michael Collins
    An Empirical Study of Semi-supervised Structured Conditional Models for Dependency Parsing
    Syntax: Take a look at earlier model in Suzuki, ACL'08. What is with the q function? Other work building on McDonald: Carreras '07, Koo '08. MIRA training.

  • D09-1059 [bib]: Richard Johansson
    Statistical Bistratal Dependency Parsing
    Syntax: Trying simultaneous parsing/SRL with joint probabilistic model.

  • D09-1060 [bib]: Wenliang Chen; Jun’ichi Kazama; Kiyotaka Uchimoto; Kentaro Torisawa
    Improving Dependency Parsing with Subtrees from Auto-Parsed Data
    Syntax: Self training, SSL for parser. Improvement, even though confidence in unlabeled text not well represented. Best system does 46% of the sentences completely correct (unlabeled).

  • D09-1065 [bib]: Brian Murphy; Marco Baroni; Massimo Poesio
    EEG responds to conceptual stimuli and corpus semantics
    Brain: Using EEG instead of fMRI in Mitchell style work. Why doesn't anybody try: (1) verbs, (2) grammaticality, (3) lie/truth, (4) agree/disagree, (5) complex grammatical constructs.

  • D09-1070 [bib]: Taesun Moon; Katrin Erk; Jason Baldridge
    Unsupervised morphological segmentation and clustering with document boundaries
    Mor: help unsupervised morphology by assuming same stem more likely to appear in same document.

  • D09-1071 [bib]: Jurgen Van Gael; Andreas Vlachos; Zoubin Ghahramani
    The infinite HMM for unsupervised PoS tagging
    Syntax: Use npbayes to pick the number of HMM states. Directly use learnt HMM states rather than trying to map them to existing tagset.

  • D09-1072 [bib]: Qiuye Zhao; Mitch Marcus
    A Simple Unsupervised Learner for POS Disambiguation Rules Given Only a Minimal Lexicon

  • D09-1085 [bib]: Laura Rimell; Stephen Clark; Mark Steedman
    Unbounded Dependency Recovery for Parser Evaluation
    Syntax: same motivation as Onder's work. Focuses on a particular construct difficult for parsers (accuracy < 50%) and builds a test set. Same problem in many fields (infrequent senses ignored in WSD, rare issues ignored in RTE/Semantics, rare constructs ignored in syntax, etc. etc.)

  • D09-1086 [bib]: David A. Smith; Jason Eisner
    Parser Adaptation and Projection with Quasi-Synchronous Grammar Features
    Syntax: learn mapping between parsers with different output styles (e.g. how they connect auxiliary verbs).

  • D09-1087 [bib]: Zhongqiang Huang; Mary Harper
    Self-Training PCFG Grammars with Latent Annotations Across Languages

  • D09-1088 [bib]: Reut Tsarfaty; Khalil Sima’an; Remko Scha
    An Alternative to Head-Driven Approaches for Parsing a (Relatively) Free Word-Order Language
    Syntax: Separate ordering information to get better coefficient stats in parser learning. Many issues same as Turkish.

  • D09-1105 [bib]: Roy Tromble; Jason Eisner
    Learning Linear Ordering Problems for Better Translation
    MT: Approximate solution to reordering problem for MT. Shows improvement. Does not make use of parse tree.

  • D09-1106 [bib]: Yang Liu; Tian Xia; Xinyan Xiao; Qun Liu
    Weighted Alignment Matrices for Statistical Machine Translation
    MT: Compact representation for an alignment distribution. Similar to forest for trees or lattice for segmentations.

  • D09-1107 [bib]: Matti Kääriäinen
    Sinuhe – Statistical Machine Translation using a Globally Trained Conditional Exponential Family Translation Model
    MT: New MT engine based on structured learning. Faster than moses with better TM scores, but overall lower BLEU.

  • D09-1108 [bib]: Hui Zhang; Min Zhang; Haizhou Li; Chew Lim Tan
    Fast Translation Rule Matching for Syntax-based Statistical Machine Translation
    MT: Compact representation with fast search for packed forests.

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