We present a Parallel Iterative Edit (PIE) model for the problem of local sequence transduction arising in tasks like Grammatical error correction (GEC). Recent approaches are based on the popular encoder-decoder (ED) model for sequence to sequence learning. The ED model auto-regressively captures full dependency among output tokens but is slow due to sequential decoding. The PIE model does parallel decoding, giving up the advantage of modelling full dependency in the output, yet it achieves accuracy competitive with the ED model for four reasons: 1. predicting edits instead of tokens, 2. labeling sequences instead of generating sequences, 3. iteratively refining predictions to capture dependencies, and 4. factorizing logits over edits and their token argument to harness pretrained language models like BERT. Experiments on tasks spanning GEC, OCR correction and spell correction demonstrate that the PIE model is an accurate and significantly faster alternative for local sequence transduction. The code and pre-trained models for GEC are available at https://github. com/awasthiabhijeet/PIE.
Archives of letters and documents belonging to individuals provide valuable insights into history. In the digital age, such history is being captured in personal digital archives, especially in the form of email. Archival organizations have recognized the importance of email archives and often collect email when they acquire the papers of eminent donors; however they find it difficult to screen, process and provide access to email for research, due to its sheer volume. We describe the considerations we encountered with the email archives of two prominent individuals in the special collections of Stanford University Libraries. We have designed novel approaches to the challenges of (1) Reconciliation with authority records, (2) Making "finding aids" of the archive available to the general public, without revealing confidential information, and (3) Browsing an email archive when one may not know what exactly to look for.Our solutions have been implemented in a publicly available and open source system called ePADD. As a result, we enable donors and archival organizations to appraise, process and screen large-scale email archives, thereby unlocking the historical value embedded in them.
In the practice of sequential decision making, agents are often designed to sense state at regular intervals of π time steps, π > 1, ignoring state information in between sensing steps. While it is clear that this practice can reduce sensing and compute costs, recent results indicate a further benefit. On many Atari console games, reinforcement learning (RL) algorithms deliver substantially better policies when run with π > 1-in fact with π even as high as 180. In this paper, we investigate the role of the parameter π in RL; π is called the "frame-skip" parameter, since states in the Atari domain are images. For evaluating a fixed policy, we observe that under standard conditions, frame-skipping does not affect asymptotic consistency. Depending on other parameters, it can possibly even benefit learning. To use π > 1 in the control setting, one must first specify which π-step open-loop action sequences can be executed in between sensing steps. We focus on "action-repetition", the common restriction of this choice to π-length sequences of the same action. We define a task-dependent quantity called the "price of inertia", in terms of which we upper-bound the loss incurred by action-repetition. We show that this loss may be offset by the gain brought to learning by a smaller task horizon. Our analysis is supported by experiments on different tasks and learning algorithms.
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