Table-to-text generation aims to generate a description for a factual table which can be viewed as a set of field-value records. To encode both the content and the structure of a table, we propose a novel structure-aware seq2seq architecture which consists of field-gating encoder and description generator with dual attention. In the encoding phase, we update the cell memory of the LSTM unit by a field gate and its corresponding field value in order to incorporate field information into table representation. In the decoding phase, dual attention mechanism which contains word level attention and field level attention is proposed to model the semantic relevance between the generated description and the table. We conduct experiments on the WIKIBIO dataset which contains over 700k biographies and corresponding infoboxes from Wikipedia. The attention visualizations and case studies show that our model is capable of generating coherent and informative descriptions based on the comprehensive understanding of both the content and the structure of a table. Automatic evaluations also show our model outperforms the baselines by a great margin. Code for this work is available on this https URL
Our model borrowed the idea of representing a structured table by its field and content information from @cite_13 . However, their n-gram model is inefficient to model long-range dependencies while generating descriptions. mei2015talk also proposed a seq2seq model with an aligner between weather records and weather broadcast. The model used one-hot encoding to represent the weather records as they are relatively simple and highly structured. However, the model is not capable to represent the tables with complex structure like Wikipedia infoboxes.
@cite_13
2962905474 2204900930 1498481277 2963105378
We propose an end-to-end, domainindependent neural encoder-aligner-decoder model for selective generation, i.e., the joint task of content selection and surface realization. Our model first encodes a full set of over-determined database event records via an LSTM-based recurrent neural network, then utilizes a novel coarse-to-fine aligner to identify the small subset of salient records to talk about, and finally employs a decoder to generate free-form descriptions of the aligned, selected records. Our model achieves the best selection and generation results reported to-date (with 59 relative improvement in generation) on the benchmark WEATHERGOV dataset, despite using no specialized features or linguistic resources. Using an improved k-nearest neighbor beam filter helps further. We also perform a series of ablations and visualizations to elucidate the contributions of our key model components. Lastly, we evaluate the generalizability of our model on the ROBOCUP dataset, and get results that are competitive with or better than the state-of-the-art, despite being severely data-starved. We propose an end-to-end, domain-independent neural encoder-aligner-decoder model for selective generation, i.e., the joint task of content selection and surface realization. Our model first encodes a full set of over-determined database event records via an LSTM-based recurrent neural network, then utilizes a novel coarse-to-fine aligner to identify the small subset of salient records to talk about, and finally employs a decoder to generate free-form descriptions of the aligned, selected records. Our model achieves the best selection and generation results reported to-date (with 59 relative improvement in generation) on the benchmark WeatherGov dataset, despite using no specialized features or linguistic resources. Using an improved k-nearest neighbor beam filter helps further. We also perform a series of ablations and visualizations to elucidate the contributions of our key model components. Lastly, we evaluate the generalizability of our model on the RoboCup dataset, and get results that are competitive with or better than the state-of-the-art, despite being severely data-starved. In round-by-round models of distributed computing processes run in a sequence of (synchronous or asynchronous) rounds. The advantage of the round-by-round approach is that invariants established in the first round are preserved in later rounds. An elegant asynchronous round-by-round shared memory model, is the iterated snapshots model (IS). Instead of the snapshots model where processes share an array m[ċ] that can be accessed any number of times, indexed by process ID, where Pi writes to m[i] and can take a snapshot of the entire array, we have processes share a two-dimensional array m[ċ, ċ], indexed by iteration number and by process ID, where Pi in iteration r writes once to m[r, i] and takes one snapshot of row r, m[r, ċ]. The IS model lends itself more easily to combinatorial analysis. However, to show that whenever a task is impossible in the IS model the task is impossible in the snapshots model, a simulation is needed. Such a simulation was presented by Borowsky and Gafni in PODC97; namely, it was shown how to take a wait-free protocol for the snapshots model, and transform it into a protocol for the IS model, solving the same task. In this paper we present a new simulation from the snapshots model to the IS model, and show that it can be extended to work with models stronger that wait-free. The main contribution is to show that the simulation can work with models that have access to certain communication objects, called 01-tasks. This extends the result of Gafni, Rajsbaum and Herlihy in DISC'2006 stating that renaming is strictly weaker than set agreement from the IS model to the usual non-iterated wait-free read write shared memory model. We also show that our simulation works with t-resilient models and the more general dependent process failure model of Junqueira and Marzullo. This version of the simulation extends previous results by Herlihy and Rajsbaum in PODC'2010 and DISC'2010 about the topological connectivity of a protocol complex in an iterated dependent process failure model, to the corresponding non-iterated model. For statistical learning, categorical variables in a table are usually considered as discrete entities and encoded separately to feature vectors, e.g., with one-hot encoding. “Dirty” non-curated data give rise to categorical variables with a very high cardinality but redundancy: several categories reflect the same entity. In databases, this issue is typically solved with a deduplication step. We show that a simple approach that exposes the redundancy to the learning algorithm brings significant gains. We study a generalization of one-hot encoding, similarity encoding, that builds feature vectors from similarities across categories. We perform a thorough empirical validation on non-curated tables, a problem seldom studied in machine learning. Results on seven real-world datasets show that similarity encoding brings significant gains in predictive performance in comparison with known encoding methods for categories or strings, notably one-hot encoding and bag of character n-grams. We draw practical recommendations for encoding dirty categories: 3-gram similarity appears to be a good choice to capture morphological resemblance. For very high-cardinalities, dimensionality reduction significantly reduces the computational cost with little loss in performance: random projections or choosing a subset of prototype categories still outperform classic encoding approaches.