Unsupervised does not mean uninterpretable : the case for word sense induction and disambiguation

This dataset contains the models for interpretable Word Sense Disambiguation (WSD) that were employed in Panchenko et al. (2017; the paper can be accessed at https://www.lt.informatik.tu-darmstadt.de/fileadmin/user_upload/Group_LangTech/publications/EACL_Interpretability___FINAL__1_.pdf). The files were computed on a 2015 dump from the English Wikipedia. Their contents: Induced Sense Inventories: wp_stanford_sense_inventories.tar.gz This file contains 3 inventories (coarse, medium fine) Language Model (3-gram): wiki_text.3.arpa.gz This file contains all n-grams up to n=3 and can be loaded into an index Weighted Dependency Features: wp_stanford_lemma_LMI_s0.0_w2_f2_wf2_wpfmax1000_wpfmin2_p1000.gz This file contains weighted word--context-feature combinations and includes their count and an LMI significance score Distributional Thesaurus (DT) of Dependency Features: wp_stanford_lemma_BIM_LMI_s0.0_w2_f2_wf2_wpfmax1000_wpfmin2_p1000_simsortlimit200_feature expansion.gz This file contains a DT of context features. The context feature similarities can be used for context expansion For further information, consult the paper and the companion page: http://jobimtext.org/wsd/ Panchenko A., Ruppert E., Faralli S., Ponzetto S. P., and Biemann C. (2017): Unsupervised Does Not Mean Uninterpretable: The Case for Word Sense Induction and Disambiguation. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics (EACL'2017). Valencia, Spain. Association for Computational Linguistics

Unsupervised Sense-Aware Hypernymy Extraction

In this paper, we show how unsupervised sense representations can be used to improve hypernymy extraction. We present a method for extracting disambiguated hypernymy relationships that propagates hypernyms to sets of synonyms (synsets), constructs embeddings for these sets, and establishes sense-aware relationships between matching synsets. Evaluation on two gold standard datasets for English and Russian shows that the method successfully recognizes hypernymy relationships that cannot be found with standard Hearst patterns and Wiktionary datasets for the respective languages.Comment: In Proceedings of the 14th Conference on Natural Language Processing (KONVENS 2018). Vienna, Austri

Tracking of enriched dialog states for flexible conversational information access

Dialog state tracking (DST) is a crucial component in a task-oriented dialog system for conversational information access. A common practice in current dialog systems is to define the dialog state by a set of slot-value pairs. Such representation of dialog states and the slot-filling based DST have been widely employed, but suffer from three drawbacks. (1) The dialog state can contain only a single value for a slot, and (2) can contain only users' affirmative preference over the values for a slot. (3) Current task-based dialog systems mainly focus on the searching task, while the enquiring task is also very common in practice. The above observations motivate us to enrich current representation of dialog states and collect a brand new dialog dataset about movies, based upon which we build a new DST, called enriched DST (EDST), for flexible accessing movie information. The EDST supports the searching task, the enquiring task and their mixed task. We show that the new EDST method not only achieves good results on Iqiyi dataset, but also outperforms other state-of-the-art DST methods on the traditional dialog datasets, WOZ2.0 and DSTC2.Comment: 5 pages, 2 figures, accepted by ICASSP201

Paradigm Completion for Derivational Morphology

The generation of complex derived word forms has been an overlooked problem in NLP; we fill this gap by applying neural sequence-to-sequence models to the task. We overview the theoretical motivation for a paradigmatic treatment of derivational morphology, and introduce the task of derivational paradigm completion as a parallel to inflectional paradigm completion. State-of-the-art neural models, adapted from the inflection task, are able to learn a range of derivation patterns, and outperform a non-neural baseline by 16.4%. However, due to semantic, historical, and lexical considerations involved in derivational morphology, future work will be needed to achieve performance parity with inflection-generating systems.Comment: EMNLP 201

Neural Natural Language Inference Models Enhanced with External Knowledge

Modeling natural language inference is a very challenging task. With the availability of large annotated data, it has recently become feasible to train complex models such as neural-network-based inference models, which have shown to achieve the state-of-the-art performance. Although there exist relatively large annotated data, can machines learn all knowledge needed to perform natural language inference (NLI) from these data? If not, how can neural-network-based NLI models benefit from external knowledge and how to build NLI models to leverage it? In this paper, we enrich the state-of-the-art neural natural language inference models with external knowledge. We demonstrate that the proposed models improve neural NLI models to achieve the state-of-the-art performance on the SNLI and MultiNLI datasets.Comment: Accepted by ACL 201