We present a model for semantic proto-role labeling (SPRL) using an adapted bidirectional LSTM encoding strategy that we call Neural-Davidsonian: predicate-argument structure is represented as pairs of hidden states corresponding to predicate and argument head tokens of the input sequence. We demonstrate:(1) state-of-the-art results in SPRL, and (2) that our network naturally shares parameters between attributes, allowing for learning new attribute types with limited added supervision. The cat ate the rat Word Embeddings BiLSTM Wshared ReLU Wchanged_state Wvolition Wexisted_after hate hrat Neural Davidsonian Semantic Proto-roles changed_state(eate, rat) existed_after(eate, rat) volition(eate, rat)2 Implementation available at https://github. com/decomp-sem/neural-sprl.
PSODA is a comprehensive phylogenetics package, including alignment, phylogenetic search under both parsimony and maximum likelihood, and visualisation and analysis tools. PSODA offers performance comparable to PAUP* in an open source package that aims to provide a foundation for researchers examining new phylogenetic algorithms. A key new feature is PsodaScript, an extension to the nearly ubiquitous NEXUS format, that includes conditional and loop constructs; thereby allowing complex meta-search techniques like the parsimony ratchet to be easily and compactly implemented. PSODA promises to be a valuable tool in the future development of novel phylogenetic techniques. This paper seeks to familiarise researchers with PSODA and its features, in particular the internal scripting language, PsodaScript. PSODA is freely available from the PSODA.
The semantic function tags of Bonial, Stowe, and Palmer (2013) and the ordinal, multi-property annotations of Reisinger et al. (2015) draw inspiration from Ddowty's semantic proto-role theory. We approach proto-role labeling as a multi-label classification problem and establish strong results for the task by adapting a successful model of traditional semantic role labeling. We achieve a proto-role micro-averaged F1 of 81.7 using gold syntax and explore joint and conditional models of proto-roles and categorical roles. In comparing the effect of Bonial, Stowe, and Palmer's tags to PropBank ArgN-style role labels, we are surprised that neither annotations greatly improve proto-role prediction; however, we observe that ArgN models benefit much from observed syntax and from observed or modeled proto-roles while our models of the semantic function tags do not.
Machine translation (MT) draws from several different disciplines, making it a complex subject to teach. There are excellent pedagogical texts, but problems in MT and current algorithms for solving them are best learned by doing. As a centerpiece of our MT course, we devised a series of open-ended challenges for students in which the goal was to improve performance on carefully constrained instances of four key MT tasks: alignment, decoding, evaluation, and reranking. Students brought a diverse set of techniques to the problems, including some novel solutions which performed remarkably well. A surprising and exciting outcome was that student solutions or their combinations fared competitively on some tasks, demonstrating that even newcomers to the field can help improve the state-of-the-art on hard NLP problems while simultaneously learning a great deal. The problems, baseline code, and results are freely available.
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