Hierarchical Quantized Representations for Script Generation

Scripts define knowledge about how everyday scenarios (such as going to a restaurant) are expected to unfold. One of the challenges to learning scripts is the hierarchical nature of the knowledge. For example, a suspect arrested might plead innocent or guilty, and a very different track of events is then expected to happen. To capture this type of information, we propose an autoencoder model with a latent space defined by a hierarchy of categorical variables. We utilize a recently proposed vector quantization based approach, which allows continuous embeddings to be associated with each latent variable value. This permits the decoder to softly decide what portions of the latent hierarchy to condition on by attending over the value embeddings for a given setting. Our model effectively encodes and generates scripts, outperforming a recent language modeling-based method on several standard tasks, and allowing the autoencoder model to achieve substantially lower perplexity scores compared to the previous language modeling-based method.Comment: EMNLP 201

Sub-word indexing and blind relevance feedback for English, Bengali, Hindi, and Marathi IR

The Forum for Information Retrieval Evaluation (FIRE) provides document collections, topics, and relevance assessments for information retrieval (IR) experiments on Indian languages. Several research questions are explored in this paper: 1. how to create create a simple, languageindependent corpus-based stemmer, 2. how to identify sub-words and which types of sub-words are suitable as indexing units, and 3. how to apply blind relevance feedback on sub-words and how feedback term selection is affected by the type of the indexing unit. More than 140 IR experiments are conducted using the BM25 retrieval model on the topic titles and descriptions (TD) for the FIRE 2008 English, Bengali, Hindi, and Marathi document collections. The major findings are: The corpus-based stemming approach is effective as a knowledge-light term conation step and useful in case of few language-specific resources. For English, the corpusbased stemmer performs nearly as well as the Porter stemmer and significantly better than the baseline of indexing words when combined with query expansion. In combination with blind relevance feedback, it also performs significantly better than the baseline for Bengali and Marathi IR. Sub-words such as consonant-vowel sequences and word prefixes can yield similar or better performance in comparison to word indexing. There is no best performing method for all languages. For English, indexing using the Porter stemmer performs best, for Bengali and Marathi, overlapping 3-grams obtain the best result, and for Hindi, 4-prefixes yield the highest MAP. However, in combination with blind relevance feedback using 10 documents and 20 terms, 6-prefixes for English and 4-prefixes for Bengali, Hindi, and Marathi IR yield the highest MAP. Sub-word identification is a general case of decompounding. It results in one or more index terms for a single word form and increases the number of index terms but decreases their average length. The corresponding retrieval experiments show that relevance feedback on sub-words benefits from selecting a larger number of index terms in comparison with retrieval on word forms. Similarly, selecting the number of relevance feedback terms depending on the ratio of word vocabulary size to sub-word vocabulary size almost always slightly increases information retrieval effectiveness compared to using a fixed number of terms for different languages

Rate of photosynthetic induction in fluctuating light varies widely among genotypes of wheat.

Crop photosynthesis and yield are limited by slow photosynthetic induction in sunflecks. We quantified variation in induction kinetics across diverse genotypes of wheat for the first time. Following a preliminary study that hinted at wide variation in induction kinetics across 58 genotypes, we grew 10 genotypes with contrasting responses in a controlled environment and quantified induction kinetics of carboxylation capacity (Vcmax) from dynamic A versus ci curves after a shift from low to high light (from 50 µmol m-2 s-1 to 1500 µmol m-2 s-1), in five flag leaves per genotype. Within-genotype median time for 95% induction (t95) of Vcmax varied 1.8-fold, from 5.2 min to 9.5 min. Our simulations suggest that non-instantaneous induction reduces daily net carbon gain by up to 15%, and that breeding to speed up Vcmax induction in the slowest of our 10 genotypes to match that in the fastest genotype could increase daily net carbon gain by up to 3.4%, particularly for leaves in mid-canopy positions (cumulative leaf area index ≤1.5 m2 m-2), those that experience predominantly short-duration sunflecks, and those with high photosynthetic capacities

Visuospatial tasks suppress craving for cigarettes.

The Elaborated Intrusion (EI) theory of desire posits that visual imagery plays a key role in craving. We report a series of experiments testing this hypothesis in a drug addiction context. Experiment 1 showed that a mental visual imagery task with neutral content reduced cigarette craving in abstaining smokers, but that an equivalent auditory task did not. The effect of visual imagery was replicated in Experiment 2, which also showed comparable effects of non-imagery visual working memory interference. Experiment 3 showed that the benefit of visual over auditory interference was not dependent upon imagery being used to induce craving. Experiment 4 compared a visuomotor task, making shapes from modeling clay, with a verbal task (counting back from 100), and again showed a benefit of the visual over the non-visual task. We conclude that visual imagery supports craving for cigarettes. Competing imagery or visual working memory tasks may help tackle craving in smokers trying to quit