Task generation for testing text understanding and reasoning
One long-term goal of machine learning research is to produce methods that are applicable to reasoning and natural language, in particular building an intelligent dialogue agent. To measure progress towards that goal, we argue for the usefulness of a set of proxy tasks that evaluate reading comprehension via question answering. Our tasks measure understanding in several ways: whether a system is able to answer questions via chaining facts, simple induction, deduction and many more. The tasks are designed to be prerequisites for any system that aims to be capable of conversing with a human. We believe many existing learning systems can currently not solve them, and hence our aim is to classify these tasks into skill sets, so that researchers can identify (and then rectify) the failings of their systems. We also extend and improve the recently introduced Memory Networks model, and show it is able to solve some, but not all, of the tasks.READ FULL TEXT VIEW PDF
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Task generation for testing text understanding and reasoning
Question Answering Using Attentive Reader and Recurrent Neural Networks
Solving Question-Answering Problem Using Deep Learning
This is an implementation Of Question Answering System on bAbi DataSet
Reimplementation of Memory Networks (MemNN) in Julia using Knet. A Deep Learning project.
There is a rich history of the use of synthetic tasks in machine learning, from the XOR problem which helped motivate neural networks(Minsky & Papert, 1969; Rumelhart et al., 1985)
, to circle and ring datasets that helped motivate some of the most well-known clustering and semi-supervised learning algorithms(Ng et al., 2002; Zhu et al., 2003), Mackey Glass equations for time series (Müller et al., 1997), and so on – in fact some of the well known UCI datasets (Bache & Lichman, 2013) are synthetic as well (e.g., waveform
). Recent work continues this trend. For example, in the area of developing learning algorithms with a memory component synthetic datasets were used to help develop both the Neural Turing Machine ofGraves et al. (2014) and the Memory Networks of Weston et al. (2014), the latter of which is relevant to this work.
One of the reasons for the interest in synthetic data is that it can be easier to develop new techniques using it. It is well known that working with large amounts of real data (“big data”) tends to lead researchers to simpler models as “simple models and a lot of data trump more elaborate models based on less data” (Halevy et al., 2009). For example, -grams for language modeling work well relative to existing competing methods, but are far from being a model that truly understands text. As researchers we can become stuck in local minima in algorithm space; development of synthetic data is one way to try and break out of that.
In this work we propose a framework and a set of synthetic tasks for the goal of helping to develop learning algorithms for text understanding and reasoning. While it is relatively difficult to automatically evaluate the performance of an agent in general dialogue – a long term-goal of AI – it is relatively easy to evaluate responses to input questions, i.e., the task of question answering (QA). Question answering is incredibly broad: more or less any task one can think of can be cast into this setup. This enables us to propose a wide ranging set of different tasks, that test different capabilities of learning algorithms, under a common framework.
Our tasks are built with a unified underlying simulation of a physical world, akin to a classic text adventure game (Montfort, 2005) whereby actors move around manipulating objects and interacting with each other. As the simulation runs, grounded text and question answer pairs are simultaneously generated. Our goal is to categorize different kinds of questions into skill sets, which become our tasks. Our hope is that the analysis of performance on these tasks will help expose weaknesses of current models and help motivate new algorithm designs that alleviate these weaknesses. We further envision this as a feedback loop where new tasks can then be designed in response, perhaps in an adversarial fashion, in order to break the new models.
The tasks we design are detailed in Section 3, and the simulation used to generate them in Section 4. In Section 5 we give benchmark results of standard methods on our tasks, and analyse their successes and failures. In order to exemplify the kind of feedback loop between algorithm development and task development we envision, in Section A we propose a set of improvements to the recent Memory Network method, which has shown to give promising performance in QA. We show our proposed approach does indeed give improved performance on some tasks, but is still unable to solve some of them, which we consider as open problems.
Several projects targeting language understanding using QA-based strategies have recently emerged. Unlike tasks like dialogue or summarization, QA is easy to evaluate (especially in true/false or multiple choice scenarios) and hence makes it an appealing research avenue. The difficulty lies in the definition of questions: they must be unambiguously answerable by adult humans (or children), but still require some thinking. The Allen Institute for AI’s flagship project aristo111http://allenai.org/aristo.html is organized around a collection of QA tasks derived from increasingly difficult science exams, at the 4th, 8th, and 12th grade levels. Richardson et al. (2013) proposed the MCTest222http://research.microsoft.com/mct a set of 660 stories and associated questions intended for research on the machine comprehension of text. Each question requires the reader to understand different aspects of the story.
These two initiatives go in a promising direction but interpreting the results on these benchmarks remain complicated. Indeed, no system has yet been able to fully solve the proposed tasks and since many sub-tasks need to be solved to answer any of their questions (coreference, deduction, use of common-sense, etc.), it is difficult to clearly identify capabilities and limitations of these systems and hence to propose improvements and modifications. As a result, conclusions drawn from these projects are not much clearer than that coming from more traditional works on QA over large-scale Knowledge Bases (Berant et al., 2013; Fader et al., 2014). Besides, the best performing systems are based on hand-crafted patterns and features, and/or statistics acquired on very large corpora. It is difficult to argue that such systems actually understand language and are not simply light upgrades of traditional information extraction methods (Yao et al., 2014). The system of Berant et al. (2014) is more evolved since it builds a structured representation of a text and of a question to answer. Despite its potential this method remains highly domain specific and relies on a lot of prior knowledge.
Based on these observations, we chose to conceive a collection of much simpler QA tasks, with the main objective that failure or success of a system on any of them can unequivocally provide feedback on its capabilities. In that, we are close to the Winograd Schema Challenge Levesque et al. (2011), which is organized around simple statements followed by a single binary choice question such as: “Joan made sure to thank Susan for all the help she had received. Who had received the help? Joan or Susan?”. In this challenge, and our tasks, it is straightforward to interpret results. Yet, where the Winograd Challenge is mostly centered around evaluating if systems can acquire and make use of background knowledge that is not expressed in the words of the statement, our tasks are self-contained and are more diverse. By self-contained we mean our tasks come with both training data and evaluation data, rather than just the latter as in the case of aristo and the Winograd Challenge. MCTest has a train/test split but the training set is likely too small to capture all the reasoning needed to do well on the test set. In our setup one can assess the amount of training examples needed to perform well (which can be increased as desired) and commonsense knowledge and reasoning required for the test set should be contained in the training set. In terms of diversity, some of our tasks are related to existing setups but we also propose many additional ones; tasks 8 and 9 are inspired by previous work on lambda dependency-based compositional semantics (Liang et al., 2013; Liang, 2013) for instance. For us, each task checks one skill that the system must have and we postulate that performing well on all of them is a prerequisite for any system aiming at full text understanding and reasoning.
Our main idea is to provide a set of tasks, in a similar way to how software testing is built in computer science. Ideally each task is a “leaf” test case, as independent from others as possible, and tests in the simplest way possible one aspect of intended behavior. Subsequent (“non-leaf”) tests can build on these by testing combinations as well. The tasks are publicly available at http://fb.ai/babi. Source code to generate the tasks is available at https://github.com/facebook/bAbI-tasks.
Each task provides a set of training and test data, with the intention that a successful model performs well on test data. Following Weston et al. (2014), the supervision in the training set is given by the true answers to questions, and the set of relevant statements for answering a given question, which may or may not be used by the learner. We set up the tasks so that correct answers are limited to a single word (Q: Where is Mark? A: bathroom), or else a list of words (Q: What is Mark holding?) as evaluation is then clear-cut, and is measured simply as right or wrong.
All of the tasks are noiseless and a human able to read that language can potentially achieve 100% accuracy. We tried to choose tasks that are natural to a human: they are based on simple usual situations and no background in areas such as formal semantics, machine learning, logic or knowledge representation is required for an adult to solve them.
The data itself is produced using a simple simulation of characters and objects moving around and interacting in locations, described in Section 4. The simulation allows us to generate data in many different scenarios where the true labels are known by grounding to the simulation. For each task, we describe it by giving a small sample of the dataset including statements, questions and the true labels (in red) in Tables 1 and 2.
|Task 1: Single Supporting Fact||Task 2: Two Supporting Facts|
|Mary went to the bathroom.||John is in the playground.|
|John moved to the hallway.||John picked up the football.|
|Mary travelled to the office.||Bob went to the kitchen.|
|Where is Mary? A:office||Where is the football? A:playground|
|Task 3: Three Supporting Facts||Task 4: Two Argument Relations|
|John picked up the apple.||The office is north of the bedroom.|
|John went to the office.||The bedroom is north of the bathroom.|
|John went to the kitchen.||The kitchen is west of the garden.|
|John dropped the apple.||What is north of the bedroom? A: office|
|Where was the apple before the kitchen? A:office||What is the bedroom north of? A: bathroom|
|Task 5: Three Argument Relations||Task 6: Yes/No Questions|
|Mary gave the cake to Fred.||John moved to the playground.|
|Fred gave the cake to Bill.||Daniel went to the bathroom.|
|Jeff was given the milk by Bill.||John went back to the hallway.|
|Who gave the cake to Fred? A: Mary||Is John in the playground? A:no|
|Who did Fred give the cake to? A: Bill||Is Daniel in the bathroom? A:yes|
|Task 7: Counting||Task 8: Lists/Sets|
|Daniel picked up the football.||Daniel picks up the football.|
|Daniel dropped the football.||Daniel drops the newspaper.|
|Daniel got the milk.||Daniel picks up the milk.|
|Daniel took the apple.||John took the apple.|
|How many objects is Daniel holding? A: two||What is Daniel holding? milk, football|
|Task 9: Simple Negation||Task 10: Indefinite Knowledge|
|Sandra travelled to the office.||John is either in the classroom or the playground.|
|Fred is no longer in the office.||Sandra is in the garden.|
|Is Fred in the office? A:no||Is John in the classroom? A:maybe|
|Is Sandra in the office? A:yes||Is John in the office? A:no|
|Task 11: Basic Coreference||Task 12: Conjunction|
|Daniel was in the kitchen.||Mary and Jeff went to the kitchen.|
|Then he went to the studio.||Then Jeff went to the park.|
|Sandra was in the office.||Where is Mary? A: kitchen|
|Where is Daniel? A:studio||Where is Jeff? A: park|
|Task 13: Compound Coreference||Task 14: Time Reasoning|
|Daniel and Sandra journeyed to the office.||In the afternoon Julie went to the park.|
|Then they went to the garden.||Yesterday Julie was at school.|
|Sandra and John travelled to the kitchen.||Julie went to the cinema this evening.|
|After that they moved to the hallway.||Where did Julie go after the park? A:cinema|
|Where is Daniel? A: garden||Where was Julie before the park? A:school|
|Task 15: Basic Deduction||Task 16: Basic Induction|
|Sheep are afraid of wolves.||Lily is a swan.|
|Cats are afraid of dogs.||Lily is white.|
|Mice are afraid of cats.||Bernhard is green.|
|Gertrude is a sheep.||Greg is a swan.|
|What is Gertrude afraid of? A:wolves||What color is Greg? A:white|
|Task 17: Positional Reasoning||Task 18: Size Reasoning|
|The triangle is to the right of the blue square.||The football fits in the suitcase.|
|The red square is on top of the blue square.||The suitcase fits in the cupboard.|
|The red sphere is to the right of the blue square.||The box is smaller than the football.|
|Is the red sphere to the right of the blue square? A:yes||Will the box fit in the suitcase? A:yes|
|Is the red square to the left of the triangle? A:yes||Will the cupboard fit in the box? A:no|
|Task 19: Path Finding||Task 20: Agent’s Motivations|
|The kitchen is north of the hallway.||John is hungry.|
|The bathroom is west of the bedroom.||John goes to the kitchen.|
|The den is east of the hallway.||John grabbed the apple there.|
|The office is south of the bedroom.||Daniel is hungry.|
|How do you go from den to kitchen? A: west, north||Where does Daniel go? A:kitchen|
|How do you go from office to bathroom? A: north, west||Why did John go to the kitchen? A:hungry|
Task 1 consists of questions where a previously given single supporting fact, potentially amongst a set of other irrelevant facts, provides the answer. We first test one of the simplest cases of this, by asking for the location of a person, e.g. “Mary travelled to the office. Where is Mary?”. This kind of task was already employed in Weston et al. (2014). It can be considered the simplest case of some real world QA datasets such as in Fader et al. (2013).
A harder task is to answer questions where two supporting statements have to be chained to answer the question, as in task 2, where to answer the question “Where is the football?” one has to combine information from two sentences “John is in the playground” and “John picked up the football”. Again, this kind of task was already used in Weston et al. (2014). Similarly, one can make a task with three supporting facts, given in task 3, whereby the first three statements are all required to answer the question “Where was the apple before the kitchen?”.
To answer questions the ability to differentiate and recognize subjects and objects is crucial. In task 4 we consider the extreme case where sentences feature re-ordered words, i.e. a bag-of-words will not work. For example, the questions “What is north of the bedroom?” and “What is the bedroom north of?” have exactly the same words, but a different order, with different answers. A step further, sometimes one needs to differentiate three separate arguments. Task 5 involves statements like “Jeff was given the milk by Bill” and then queries who is the giver, receiver or which object is involved.
Task 6 tests, on some of the simplest questions possible (specifically, ones with a single supporting fact), the ability of a model to answer true/false type questions like “Is John in the playground?”.
Task 7 tests the ability of the QA system to perform simple counting operations, by asking about the number of objects with a certain property, e.g. “How many objects is Daniel holding?”. Similarly, task 8 tests the ability to produce a set of single word answers in the form of a list, e.g. “What is Daniel holding?”. These tasks can be seen as QA tasks related to basic database search operations.
Tasks 9 and 10 test slightly more complex natural language constructs. Task 9 tests one of the simplest forms of negation, that of supporting facts that imply a statement is false e.g. “Fred is no longer in the office” rather than “Fred travelled to the office”. (In this case, task 6 (yes/no questions) is a prerequisite to the task.) Task 10 tests if we can model statements that describe possibilities rather than certainties, e.g. “John is either in the classroom or the playground.”, where in that case the answer is “maybe” to the question “Is John in the classroom?”.
Task 11 tests the simplest type of coreference, that of detecting the nearest referent, e.g. “Daniel was in the kitchen. Then he went to the studio.”. Real-world data typically addresses this as a labeling problem and studies more sophisticated phenomena (Soon et al., 2001), whereas we evaluate it as in all our other tasks as a question answering problem. Task 12 (conjunctions) tests referring to multiple subjects in a single statement, e.g. “Mary and Jeff went to the kitchen.”. Task 13 tests coreference in the case where the pronoun can refer to multiple actors, e.g. “Daniel and Sandra journeyed to the office. Then they went to the garden”.
While our tasks so far have included time implicitly in the order of the statements, task 14 tests understanding the use of time expressions within the statements, e.g. “In the afternoon Julie went to the park. Yesterday Julie was at school.”, followed by questions about the order of events such as “Where was Julie before the park?”. Real-world datasets address the task of evaluating time expressions typically as a labeling, rather than a QA task, see e.g. UzZaman et al. (2012).
Task 15 tests basic deduction via inheritance of properties, e.g. “Sheep are afraid of wolves. Gertrude is a sheep. What is Gertrude afraid of?”. Task 16 similarly tests basic induction via inheritance of properties. A full analysis of induction and deduction is clearly beyond the scope of this work, and future tasks should analyse further, deeper aspects.
Task 17 tests spatial reasoning, one of many components of the classical SHRDLU system (Winograd, 1972) by asking questions about the relative positions of colored blocks. Task 18 requires reasoning about the relative size of objects and is inspired by the commonsense reasoning examples in the Winograd schema challenge (Levesque et al., 2011).
The goal of task 19 is to find the path between locations: given the description of various locations, it asks: how do you get from one to another? This is related to the work of Chen & Mooney (2011) and effectively involves a search problem.
Finally, task 20 questions, in the simplest way possible, why an agent performs an action. It addresses the case of actors being in a given state (hungry, thirsty, tired, …) and the actions they then take, e.g. it should learn that hungry people might go to the kitchen, and so on.
As already stated, these tasks are meant to foster the development and understanding of machine learning algorithms. A single model should be evaluated across all the tasks (not tuning per task) and then the same model should be tested on additional real-world tasks.
In our data release, in addition to providing the above 20 tasks in English, we also provide them (i) in Hindi; and (ii) with shuffled English words so they are no longer readable by humans. A good learning algorithm should perform similarly on all three, which would likely not be the case for a method using external resources, a setting intended to mimic a learner being first presented a language and having to learn from scratch.
All our tasks are generated with a simulation which behaves like a classic text adventure game. The idea is that generating text within this simulation allows us to ground the language used into a coherent and controlled (artificial) world. Our simulation follows those of Bordes et al. (2010); Weston et al. (2014) but is somewhat more complex.
The simulated world is composed of entities of various types (locations, objects, persons. etc.) and of various actions that operate on these entities. Entities have internal states: their location, whether they carry objects on top or inside them (e.g., tables and boxes), the mental state of actors (e.g. hungry), as well as properties such as size, color, and edibility. For locations, the nearby places that are connected (e.g. what lies to the east, or above) are encoded. For actors, a set of pre-specified rules per actor can also be specified to control their behavior, e.g. if they are hungry they may try to find food. Random valid actions can also be executed if no rule is set, e.g. walking around randomly.
The actions an actor can execute in the simulation consist of the following: go location, get object, get object1 from object2, put object1 in/on object2, give object to actor, drop object, set entitity state, look, inventory and examine object. A set of universal constraints is imposed on those actions to enforce coherence in the simulation. For example an actor cannot get something that they or someone else already has, they cannot go to a place that is not connected to the current location, cannot drop something they do not already have, and so on. Using the underlying actions, rules for actors, and their constraints, defines how actors act. For each task we limit the actions needed for that task, e.g. task 1 only needs go whereas task 2 uses go, get and drop. If we write the commands down this gives us a very simple “story” which is executable by the simulation, e.g., joe go playground; bob go office; joe get football. This example corresponds to task 2. The system can then ask questions about the state of the simulation e.g., where john?, where football? and so on. It is easy to calculate the true answers for these questions as we have access to the underlying world.
To produce more natural looking text with lexical variety from statements and questions we employ a simple automated grammar. Each verb is assigned a set of synonyms, e.g., the simulation command get is replaced with either picked up, got, grabbed or took, and drop is replaced with either dropped, left, discarded or put down. Similarly, each object and actor can have a set of replacement synonyms as well, e.g. replacing Daniel with he in task 11. Adverbs are crucial for some tasks such as the time reasoning task 14.
There are a great many aspects of language not yet modeled. For example, all sentences are so far relatively short and contain little nesting. Further, the entities and the vocabulary size is small (150 words, and typically 4 actors, 6 locations and 3 objects used per task). The hope is that defining a set of well defined tasks will help evaluate models in a controlled way within the simulated environment, which is hard to do with real data. That is, these tasks are not a substitute for real data, but should complement them, especially when developing and analysing algorithms.
|Weakly||Uses External||Strong Supervision|
|Supervised||Resources||(using supporting facts)|
No. of ex. req. 95
|1 - Single Supporting Fact||36||50||99||100||100||100||100||100||250 ex.||100|
|2 - Two Supporting Facts||2||20||74||100||100||100||100||100||500 ex.||100|
|3 - Three Supporting Facts||7||20||17||20||100||99||100||100||500 ex.||98|
|4 - Two Arg. Relations||50||61||98||71||69||100||73||100||500 ex.||80|
|5 - Three Arg. Relations||20||70||83||83||83||86||86||98||1000 ex.||99|
|6 - Yes/No Questions||49||48||99||47||52||53||100||100||500 ex.||100|
|7 - Counting||52||49||69||68||78||86||83||85||FAIL||86|
|8 - Lists/Sets||40||45||70||77||90||88||94||91||FAIL||93|
|9 - Simple Negation||62||64||100||65||71||63||100||100||500 ex.||100|
|10 - Indefinite Knowledge||45||44||99||59||57||54||97||98||1000 ex.||98|
|11 - Basic Coreference||29||72||100||100||100||100||100||100||250 ex.||100|
|12 - Conjunction||9||74||96||100||100||100||100||100||250 ex.||100|
|13 - Compound Coref.||26||94||99||100||100||100||100||100||250 ex.||100|
|14 - Time Reasoning||19||27||99||99||100||99||100||99||500 ex.||99|
|15 - Basic Deduction||20||21||96||74||73||100||77||100||100 ex.||100|
|16 - Basic Induction||43||23||24||27||100||100||100||100||100 ex.||94|
|17 - Positional Reasoning||46||51||61||54||46||49||57||65||FAIL||72|
|18 - Size Reasoning||52||52||62||57||50||74||54||95||1000 ex.||93|
|19 - Path Finding||0||8||49||0||9||3||15||36||FAIL||19|
|20 - Agent’s Motivations||76||91||95||100||100||100||100||100||250 ex.||100|
We compared the following methods on our tasks (on the English dataset): (i) an1997), (iii) Memory Networks (MemNNs) (Weston et al., 2014), (iv) some extensions of Memory Networks we will detail; and (v) a structured SVM that incorporates external labeled data from existing NLP tasks. These models belong to three separate tracks. Weakly supervised models are only given question answer pairs at training time, whereas strong supervision provides the set of supporting facts at training time (but not testing time) as well. Strongly supervised ones give accuracy upper bounds for weakly supervised models, i.e. the performance should be superior given the same model class. Methods in the last external resources track can use labeled data from other sources rather than just the training set provided, e.g. coreference and semantic role labeling tasks, as well as strong supervision. For each task we use 1000 questions for training, and 1000 for testing, and report the test accuracy. We consider a task successfully passed if accuracy is obtained333The choice of 95% (and 1000 training examples) is arbitrary..
The -gram classifier baseline is inspired by the baselines in Richardson et al. (2013) but applied to the case of producing a 1-word answer rather than a multiple choice question: we construct a bag-of--grams for all sentences in the story that share at least one word with the question, and then learn a linear classifier to predict the answer using those features444Constructing -grams from all sentences rather than using the filtered set gave worse results..
LSTMs are a popular method for sequence prediction (Sutskever et al., 2014) and outperform standard RNNs (Recurrent Neural Networks) for tasks similar to ours (Weston et al., 2014). They work by reading the story until the point they reach a question and then have to output an answer. Note that they are weakly supervised by answers only, and are hence at a disadvantage compared to strongly supervised methods or methods that use external resources.
MemNNs (Weston et al., 2014) are a recently proposed class of models that have been shown to perform well at QA. They work by a “controller” neural network performing inference over the stored memories that consist of the previous statements in the story. The original proposed model performs 2 hops of inference: finding the first supporting fact with the maximum match score with the question, and then the second supporting fact with the maximum match score with both the question and the first fact that was found. The matching function consists of mapping the bag-of-words for the question and facts into an embedding space by summing word embeddings. The word embeddings are learnt using strong supervision to optimize the QA task. After finding supporting facts, a final ranking is performed to rank possible responses (answer words) given those facts. We also consider some extensions to this model:
Adaptive memories performing a variable number of hops rather than 2, the model is trained to predict a hop or the special “STOP” class. A similar procedure can be applied to output multiple tokens as well.
-grams We tried using a bag of 3-grams rather than a bag-of-words to represent the text. In both cases the first step of the MemNN is to convert these into vectorial embeddings.
Nonlinearity We apply a classical 2-layer neural network with tanh nonlinearity in the matching function.
More details of these variants is given in Sec A of the appendix.
Finally, we built a classical cascade NLP system baseline using a structured support vector machine (SVM), which incorporates coreference resolution and semantic role labeling (SRL) preprocessing steps, which are themselves trained on large amounts of costly labeled data. The Stanford coreference system(Raghunathan et al., 2010) and the SENNA semantic role labeling (SRL) system (Collobert et al., 2011) are used to build features for the input to the SVM, trained with strong supervision to find the supporting facts, e.g. features based on words, word pairs, and the SRL verb and verb-argument pairs. After finding the supporting facts, we build a similar structured SVM for the response stage, with features tuned for that goal as well. More details are in Sec. B of the appendix.
Standard MemNNs generally outperform the -gram and LSTM baselines, which is consistent with the results in Weston et al. (2014). However they still “fail” at a number of tasks; that is, test accuracy is less than 95%. Some of these failures are expected due to insufficient modeling power as described in more detail in Sec. A.1, e.g. facts, single word answers and bag-of-words do not succeed on tasks 3, 4, 5, 7, 8 and 18. However, there were also failures on tasks we did not at first expect, for example yes/no questions (6) and indefinite knowledge (10). Given hindsight, we realize that the linear scoring function of standard MemNNs cannot model the match between query, supporting fact and a yes/no answer as this requires three-way interactions.
Columns 5-9 of Table 3 give the results for our MemNN extensions: adaptive memories (AM), -grams (NG) and nonlinearities (NL), plus combinations thereof. The adaptive approach gives a straight-forward improvement in tasks 3 and 16 because they both require more than two supporting facts, and also gives (small) improvements in 8 and 19 because they require multi-word outputs (but still remain difficult). We hence use the AM model in combination with all our other extensions in the subsequent experiments.
MemNNs with -gram modeling yield clear improvements when word order matters, e.g. tasks 4 and 15. However, -grams do not seem to be a substitute for nonlinearities in the embedding function as the NL model outperforms -grams on average, especially in the yes/no (6) and indefinite tasks (10), as explained before. On the other hand, the NL method cannot model word order and so fails e.g., on task 4. The obvious step is thus to combine these complimentary approaches: indeed AM+NG+NL (column 9) gives improved results over both, with a total of 9 tasks that have been upgraded from failure to success compared to the original MemNN model.
The structured SVM, despite having access to external resources, does not perform better, still failing at 9 tasks. It does perform better than vanilla MemNNs (without extensions) on tasks 6, 9 and 10 where the hand-built feature conjunctions capture the necessary nonlinearities. However, compared to MemNN (AM+NG+NL) it seems to do significantly worse on tasks requiring three (and sometimes, two) supporting facts (e.g. tasks 3, 16 and 2) presumably as ranking over so many possibilities introduces more mistakes. However, its non-greedy search does seem to help on other tasks, such as path finding (task 19) where search is very important. Since it relies on external resources specifically designed for English, it is unsure that it would perform as well on other languages, like Hindi, where such external resources might be of worse quality.
The final two columns (10-11) give further analysis of the AM+NG+NL MemNN method. The second to last column (10) shows the minimum number of training examples required to achieve 95% accuracy, or FAIL if this is not achieved with 1000 examples. This is important as it is not only desirable to perform well on a task, but also using the fewest number of examples (to generalize well, quickly). Most succeeding tasks require 100-500 examples. Task 8 requires 5000 examples and 7 requires 10000, hence they are labeled as FAIL. The latter task can presumably be solved by adding all the times an object is picked up, and subtracting the times it is dropped, which seems possible for an MemNN, but it does not do perfectly. Two tasks, positional reasoning 17 and path finding 19 cannot be solved even with 10000 examples, it seems those (and indeed more advanced forms of induction and deduction, which we plan to build) require a general search algorithm to be built into the inference procedure, which MemNN (and the other approaches tried) are lacking.
The last column shows the performance of AM+NG+NL MemNNs when training on all the tasks jointly, rather than just on a single one. The performance is generally encouragingly similar, showing such a model can learn many aspects of text understanding and reasoning simultaneously. The main issues are that these models still fail on several of the tasks, and use a far stronger form of supervision (using supporting facts) than is typically realistic.
We developed a set of tasks that we believe are a prerequisite to full language understanding and reasoning. While any learner that can solve these tasks is not necessarily close to full reasoning, if a learner fails on any of our tasks then there are likely real-world tasks that it will fail on too (i.e., real-world tasks that require the same kind of reasoning). Even if the situations and the language of the tasks are artificial, we believe that the mechanisms required to learn how to solve them are part of the key towards text understanding and reasoning.
This set of tasks is not a definitive set. The purpose of a simulation-based approach is to provide flexibility and control of the tasks’ construction. We grounded the tasks into language because it is then easier to understand the usefulness of the tasks and to interpret their results. However, our primary goal is to find models able to learn to detect and combine patterns in symbolic sequences. One might even want to decrease the intrinsic difficulty by removing any lexical variability and ambiguity and reason only over bare symbols, stripped down from their linguistic meaning. One could also decorrelate the long-term memory from the reasoning capabilities of systems by, for instance, arranging the supporting facts closer to the questions. In the opposing view, one could instead want to transform the tasks into more realistic stories using annotators or more complex grammars. The set of 20 tasks presented here is a subset of what can be achieved with a simulation. We chose them because they offer a variety of skills that we would like a text reasoning model to have, but we hope researchers from the community will develop more tasks of varying complexity in order to develop and analyze models that try to solve them. Transfer learning across tasks is also a very important goal, beyond the scope of this paper. We have thus made the simulator and code for the tasks publicly available for those purposes.
Our tasks are designed as a test-bed for learning methods: we provide training and test sets because we intend to evaluate the capability of models to discover how to reason from patterns hidden within them. It could be tempting to hand-code solutions for them or to use existing large-scale QA systems like Cyc (Curtis et al., 2005). They might succeed at solving them, even if our structured SVM results (a cascaded NLP system with hand-built features) show that this is not straightforward; however this is not the tasks’ purpose since those approaches would not be learning to solve them. Our experiments show that some existing machine learning methods are successful on some of the tasks, in particular Memory Networks, for which we introduced some useful extensions (in Sec. A). However, those models still fail on several of the tasks, and use a far stronger form of supervision (using supporting facts) than is typically realistic.
These datasets are not yet solved. Future research should aim to minimize the amount of required supervision, as well as the number of training examples needed to solve a new task, to move closer to the task transfer capabilities of humans. That is, in the weakly supervised case with only 1000 training examples or less there is no known general (i.e. non-hand engineered) method that solves the tasks. Further, importantly, our hope is that a feedback loop of developing more challenging tasks, and then algorithms that can solve them, leads us to fruitful research directions.
Note that these tasks are not a substitute for real data, but should complement them, especially when developing and analysing algorithms. There are many complementary real-world datasets, see for example Hermann et al. (2015); Bordes et al. (2015); Hill et al. (2015). That is, even if a method works well on our 20 tasks, it should be shown to be useful on real data as well.
Since being online, the bAbI tasks have already directly influenced the development of several promising new algorithms, including weakly supervised end-to-end Memory Networks (MemN2N) of Sukhbaatar et al. (2015), Dynamic Memory Networks of Kumar et al. (2015), and the Neural Reasoner (Peng et al., 2015). MemN2N has since been shown to perform well on some real-world tasks (Hill et al., 2015).
On spectral clustering: Analysis and an algorithm.Advances in neural information processing systems, 2:849–856, 2002.
Memory Networks Weston et al. (2014) are a promising class of models, shown to perform well at QA, that we can apply to our tasks. They consist of a memory (an array of objects indexed by ) and four potentially learnable components , , and that are executed given an input:
(input feature map) – convert input sentence to an internal feature representation .
(generalization) – update the current memory state given the new input: .
(output feature map) – compute output given the new input and the memory: .
(response) – finally, decode output features to give the final textual response to the user: .
Potentially, component can make use of standard pre-processing, e.g., parsing and entity resolution, but the simplest form is to do no processing at all. The simplest form of is store the new incoming example in an empty memory slot, and leave the rest of the memory untouched. Thus, in Weston et al. (2014) the actual implementation used is exactly this simple form, where the bulk of the work is in the and components. The former is responsible for reading from memory and performing inference, e.g., calculating what are the relevant memories to answer a question, and the latter for producing the actual wording of the answer given .
The module produces output features by finding supporting memories given . They use . For the highest scoring supporting memory is retrieved with:
where is a function that scores the match between the pair of sentences and . For the case they then find a second supporting memory given the first found in the previous iteration:
where the candidate supporting memory is now scored with respect to both the original input and the first supporting memory, where square brackets denote a list. The final output is , which is input to the module .
Finally, needs to produce a textual response . While the authors also consider Recurrent Neural Networks (RNNs), their standard setup limits responses to be a single word (out of all the words seen by the model) by ranking them:
where is the set of all words in the dictionary, and is a function that scores the match.
The scoring functions and have the same form, that of an embedding model:
where is a matrix where is the number of features and is the embedding dimension. The role of and is to map the original text to the -dimensional feature space. They choose a bag of words representation, and for , i.e., every word in the dictionary has three different representations: one for and two for depending on whether the words of the input arguments are from the actual input or from the supporting memories so that they can be modeled differently.
They consider various extensions of their model, in particular modeling write time and modeling unseen words. Here we only discuss the former which we also use. In order for the model to work on QA tasks over stories it needs to know which order the sentences were uttered which is not available in the model directly. They thus add extra write time extra features to
which take on the value 0 or 1 indicating which sentence is older than another being compared, and compare triples of pairs of sentences and the question itself. Training is carried out by stochastic gradient descent using supervision from both the question answer pairs and the supporting memories (to selectand ). See Weston et al. (2014) for more details.
The Memory Networks models defined in (Weston et al., 2014) are one possible technique to try on our tasks, however there are several tasks which they are likely to fail on:
They model sentences with a bag of words so are likely to fail on tasks such as the 2-argument (task 4) and 3-argument (task 5) relation problems.
They perform only two max operations () so they cannot handle questions involving more than two supporting facts such as tasks 3 and 7.
Unless a RNN is employed in the R module, they are unable to provide multiple answers in the standard setting using eq. (3). This is required for the list (8) and path finding (19) tasks.
We therefore propose improvements to their model in the following section.
We consider a variable number of supporting facts that is automatically adapted dependent on the question being asked. To do this we consider scoring a special fact . Computation of supporting memories then becomes:
That is, we keep predicting supporting facts , conditioning at each step on the previously found facts, until is predicted at which point we stop. has its own unique embedding vector, which is also learned. In practice we still impose a hard maximum number of loops in our experiments to avoid fail cases where the computation never stops (in our experiments we use a limit of 10).
We use a similar trick for the response module as well in order to output multiple words. That is, we add a special word to the dictionary and predict word on each iteration conditional on the previous words, i.e., , until we predict .
There are several ways of modeling sentences that go beyond a bag-of-words, and we explore three variants here. The simplest is a bag-of--grams, we consider and in the bag. The main disadvantage of such a method is that the dictionary grows rapidly with . We therefore consider an alternative neural network approach, which we call a multilinear map. Each word in a sentence is binned into one of positions with where is the position of the word in a sentence of length , and for each position we employ a matrix . We then model the matching score with:
whereby we apply a linear map for each word dependent on its position, followed by a nonlinearity on the sum of mappings. Note that this is related to the model of (Yu et al., 2014) who consider tags rather than positions. While the results of this method are not shown in the main paper due to space restrictions, it performs similarly well to -grams to and may be useful in real-world cases where -grams cause the dictionary to be too large. Comparing to Table 3 MemNN with adaptive memories (AM) + multilinear obtains a mean performance of 93, the same as MemNNs with AM+NG+NL (i.e., using N-grams instead).
Finally, to assess the performance of nonlinear maps that do not model word position at all we also consider the following nonlinear embedding:
where is a matrix. This is similar to a classical two-layer neural network, but applied to both sides and of . We also consider the straight-forward combination of bag-of--grams followed by this nonlinearity.
We also built a classical cascade NLP system baseline using a structured SVM, which incorporates coreference resolution and semantic role labeling preprocessing steps, which are themselves trained on large amounts of costly labeled data. We first run the Stanford coreference system (Raghunathan et al., 2010) on the stories and each mention is then replaced with the first mention of its entity class. Second, the SENNA semantic role labeling system (SRL) (Collobert et al., 2011) is run, and we collect the set of arguments for each verb. We then define a ranking task for finding the supporting facts (trained using strong supervision):
where given the question we find at most three supporting facts with indices from the set of facts in the story (we also consider selecting an “empty fact” for the case of less than three), and is a linear scoring function with parameters . Computing the argmax requires doing exhaustive search, unlike e.g. the MemNN method which is greedy. For scalability, we thus prune the set of possible matches by requiring that facts share one common non-determiner word with each other match or with . is constructed as a set of indicator features. For simplicity each of the features only looks at pairs of sentences, i.e. . The feature function is made up of the following feature types, shown here for : (1) Word pairs: One indicator variable for each pair of words in and . (2) Pair distance: Indicator for the distance between the sentence, i.e. . (3) Pair order: Indicator for the order of the sentence, i.e. . (4) SRL Verb Pair: Indicator variables for each pair of SRL verbs in and . (5) SRL Verb-Arg Pair: Indicator variables for each pair of SRL arguments in , and their corresponding verbs. After finding the supporting facts, we build a similar structured SVM for the response stage, also with features tuned for that goal: Words – indicator for each word in , Word Pairs – indicator for each pair of words in and supporting facts, and similar SRL Verb and SRL Verb-Arg Pair features as before.
Results are given in Table 3. The structured SVM, despite having access to external resources, does not perform better than MemNNs overall, still failing at 9 tasks. It does perform well on tasks 6, 9 and 10where the hand-built feature conjunctions capture the necessary nonlinearities that the original MemNNs do not. However, it seems to do significantly worse on tasks requiring three (and sometimes, two) supporting facts (e.g. tasks 3, 16 and 2) presumably as ranking over so many possibilities introduces more mistakes. However, its non-greedy search does seem to help on other tasks, such as path finding (task 19) where search is very important.