Our goal is to better comprehend procedural text, e.g., a paragraph about photosynthesis, by not only predicting what happens, but why some actions need to happen before others. Our approach builds on a prior process comprehension framework for predicting actions' effects, to also identify subsequent steps that those effects enable. We present our new model (XPAD) that biases effect predictions towards those that (1) explain more of the actions in the paragraph and (2) are more plausible with respect to background knowledge. We also extend an existing benchmark dataset for procedural text comprehension, ProPara, by adding the new task of explaining actions by predicting their dependencies. We find that XPAD significantly outperforms prior systems on this task, while maintaining the performance on the original task in ProPara. The dataset is available at http://data.allenai.org/propara

Most real-world networks are incompletely observed. Algorithms that can accurately predict which links are missing can dramatically speedup the collection of network data and improve the validity of network models. Many algorithms now exist for predicting missing links, given a partially observed network, but it has remained unknown whether a single best predictor exists, how link predictability varies across methods and networks from different domains, and how close to optimality current methods are. We answer these questions by systematically evaluating 203 individual link predictor algorithms, representing three popular families of methods, applied to a large corpus of 548 structurally diverse networks from six scientific domains. We first show that individual algorithms exhibit a broad diversity of prediction errors, such that no one predictor or family is best, or worst, across all realistic inputs. We then exploit this diversity via meta-learning to construct a series of "stacked" models that combine predictors into a single algorithm. Applied to a broad range of synthetic networks, for which we may analytically calculate optimal performance, these stacked models achieve optimal or nearly optimal levels of accuracy. Applied to real-world networks, stacked models are also superior, but their accuracy varies strongly by domain, suggesting that link prediction may be fundamentally easier in social networks than in biological or technological networks. These results indicate that the state-of-the-art for link prediction comes from combining individual algorithms, which achieves nearly optimal predictions. We close with a brief discussion of limitations and opportunities for further improvement of these results.

The recently introduced Tsetlin Machine (TM) has provided competitive pattern recognition accuracy in several benchmarks, however, requires a 3-dimensional hyperparameter search. In this paper, we introduce the Multigranular Tsetlin Machine (MTM). The MTM eliminates the specificity hyperparameter, used by the TM to control the granularity of the conjunctive clauses that it produces for recognizing patterns. Instead of using a fixed global specificity, we encode varying specificity as part of the clauses, rendering the clauses multigranular. This makes it easier to configure the TM because the dimensionality of the hyperparameter search space is reduced to only two dimensions. Indeed, it turns out that there is significantly less hyperparameter tuning involved in applying the MTM to new problems. Further, we demonstrate empirically that the MTM provides similar performance to what is achieved with a finely specificity-optimized TM, by comparing their performance on both synthetic and real-world datasets.

Human learning comes in two forms, a fast and a slow one. The slow one requires a lot of repetition which seems to be necessary to conquer a new cognitive field such as learning a new language. But once a field is mastered, learning new facts within this field requires very few, possibly even only one example. It appears, that the brain regions involved in processing this field have been pre wired to the regions they depend on. So once a new fact needs to be learned, this pre wiring is used to speed up the training of the neurons involved in processing this new fact.

Counterfactual reasoning requires predicting how alternative events, contrary to what actually happened, might have resulted in different outcomes. Despite being considered a necessary component of AI-complete systems, few resources have been developed for evaluating counterfactual reasoning in narratives. In this paper, we propose Counterfactual Story Rewriting: given an original story and an intervening counterfactual event, the task is to minimally revise the story to make it compatible with the given counterfactual event. Solving this task will require deep understanding of causal narrative chains and counterfactual invariance, and integration of such story reasoning capabilities into conditional language generation models. We present TimeTravel, a new dataset of 29,849 counterfactual rewritings, each with the original story, a counterfactual event, and human-generated revision of the original story compatible with the counterfactual event. Additionally, we include 80,115 counterfactual "branches" without a rewritten storyline to support future work on semi- or un-supervised approaches to counterfactual story rewriting. Finally, we evaluate the counterfactual rewriting capacities of several competitive baselines based on pretrained language models, and assess whether common overlap and model-based automatic metrics for text generation correlate well with human scores for counterfactual rewriting.

Consequently, we have seen progressively improving performances on benchmarks such as GLUE (Wang et al., 2018). But, are models really becoming better? We take the position that, while tracking performance on a leaderboard is necessary to characterize model quality, it is not sufficient. Reasoning about language requires that a system has the ability not only to draw correct inferences about textual inputs, but also to be consistent its beliefs across various inputs. To illustrate this notion of consistency, let us consider the task of natural language inference (NLI) which seeks to identify whether a premise entails, contradicts or is unrelated to a hypothesis (Dagan et al., 2013).

Unsupervised learning is an old and well-understood problem in machine learning; LeCun's choice to replace it as the star in his cake analogy is not something he should take lightly! If you dive into the definition of self-supervised learning, you'll begin to see that it's really just an approach to unsupervised learning. Since many of the breakthroughs in machine learning this decade have been based on supervised learning techniques, successes in unsupervised problems tend to emerge when researchers re-frame an unsupervised problem as a supervised problem. Specifically, in self-supervised learning, we find a clever way to generate labels without human annotators. An easy example is a technique called next-step prediction.

Many question answering (QA) tasks only provide weak supervision for how the answer should be computed. For example, TriviaQA answers are entities that can be mentioned multiple times in supporting documents, while DROP answers can be computed by deriving many different equations from numbers in the reference text. In this paper, we show it is possible to convert such tasks into discrete latent variable learning problems with a precomputed, task-specific set of possible "solutions" (e.g. different mentions or equations) that contains one correct option. We then develop a hard EM learning scheme that computes gradients relative to the most likely solution at each update. Despite its simplicity, we show that this approach significantly outperforms previous methods on six QA tasks, including absolute gains of 2--10%, and achieves the state-of-the-art on five of them. Using hard updates instead of maximizing marginal likelihood is key to these results as it encourages the model to find the one correct answer, which we show through detailed qualitative analysis.

Supervised learning – A blessing we have in this machines era. It helps to depict inputs to outputs. It uses labelled training data to deduce a function which has a set of training examples. The majority of practical machine learning uses supervised learning as on date. AILabPage defines Machine Learning as "A focal point where business, data and experience meets emerging technology and decides to work together".

As a member of a research group involved in computer vision, I wanted to write this short article to briefly present what we call "Zero-shot learning" (ZSL), an interesting variant of transfer learning, and the current research related to it. Today, many machine learning methods focus on classifying instances whose classes have already been seen in training. Concretely, many applications require classifying instances whose classes have not been seen before. Zero-shot learning is a promising learning method, in which the classes covered by training instances and the classes we aim to classify are disjoint. In other words, Zero-shot learning is about leveraging supervised learning with no additional training data.