Difference between revisions of "Dialog-based Language Learning"

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# B. C. Stadie, S. Levine, and P. Abbeel. Incentivizing exploration in reinforcement learning with deep predictive models. arXiv preprint arXiv:1507.00814, 2015.
 
# B. C. Stadie, S. Levine, and P. Abbeel. Incentivizing exploration in reinforcement learning with deep predictive models. arXiv preprint arXiv:1507.00814, 2015.
 
# J. Clarke, D. Goldwasser, M.-W. Chang, and D. Roth. Driving semantic parsing from the world’s response. In Proceedings of the fourteenth conference on computational natural language learning, pages 18–27. Association for Computational Linguistics, 2010.
 
# J. Clarke, D. Goldwasser, M.-W. Chang, and D. Roth. Driving semantic parsing from the world’s response. In Proceedings of the fourteenth conference on computational natural language learning, pages 18–27. Association for Computational Linguistics, 2010.
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# J. Schatzmann, K. Weilhammer, M. Stuttle, and S. Young. A survey of statistical user simulation techniques for reinforcement-learning of dialogue management strategies. The knowledge engineering review, 21(02):97–126, 2006.

Revision as of 14:35, 7 November 2017

Note: Do not start editing until 8th Nov 2017

This page will be published for editing by EOD 7th Nov 2017 This page is a summary for NIPS 2016 paper Dialog-based Language Learning [1].

Introduction

One of the ways humans learn language, especially second language or language learning by students, is by communication and getting its feedback. However, most existing research in Natural Language Understanding has focused on supervised learning from fixed training sets of labeled data. This kind of supervision is not realistic of how humans learn, where language is both learned by, and used for, communication. When humans act in dialogs (i.e., make speech utterances) the feedback from other human’s responses contain very rich information. This is perhaps most pronounced in a student/teacher scenario where the teacher provides positive feedback for successful communication and corrections for unsuccessful ones.

This paper is about dialog-based language learning, where supervision is given naturally and implicitly in the response of the dialog partner during the conversation. This paper is a step towards the ultimate goal of being able to develop an intelligent dialog agent that can learn while conducting conversations. Specifically this paper explores whether we can train machine learning models to learn from dialog.

Contributions of this paper

  • Introduce a set of tasks that model natural feedback from a teacher and hence assess the feasibility of dialog-based language learning.
  • Evaluated some baseline models on this data and compared them to standard supervised learning.
  • Introduced a novel forward prediction model, whereby the learner tries to predict the teacher’s replies to its actions, which yields promising results, even with no reward signal at all

Background on Memory Networks


DB F1.png


A memory network combines learning strategies from the machine learning literature with a memory component that can be read and written to.

The high-level view of a memory network is as follows:

  • There is a memory, $m$, an indexed array of objects (e.g. vectors or arrays of strings).
  • An input feature map $I$, which converts the incoming input to the internal feature representation
  • A generalization component $G$ which updates old memories given the new input.
  • An output feature map $O$, which produces a new output in the feature representation space given the new input and the current memory state.
  • A response component $R$ which converts the output into the response format desired – for example, a textual response or an action.

$I$, $G$, $O$ and $R$ can all potentially be learned components and make use of any ideas from the existing machine learning literature.

In question answering systems for example, the components may be instantiated as follows:

  • $I$ can make use of standard pre-processing such as parsing, coreference, and entity resolution. It could also encode the input into an internal feature representation by converting from text to a sparse or dense feature vector.
  • The simplest form of $G$ is to introduce a function $H$ which maps the internal feature representation produced by I to an individual memory slot, and just updates the memory at H(I(x)).
  • $O$ Reads from memory and performs inference to deduce the set of relevant memories needed to perform a good response.
  • $R$ would produce the actual wording of the question answer based on the memories found by $O$. For example, $R$ could be an RNN conditioned on the output of $O$

When the components $I$,$G$,$O$, & $R$ are neural networks, the authors describe the resulting system as a Memory Neural Network (MemNN). They build a MemNN for QA (question answering) problems and compare it to RNNs (Recurrent Neural Network) and LSTMs (Long Short Term Memory RNNs) and find that it gives superior performance.

DB F2.png

Related Work

Usefulness of feedback in language learning: Social interaction and natural infant directed conversations are shown to useful for language learning[2]. Several studies[3][4][5][6] has shown that feedback is especially useful in second language learning and learning by students.

Supervised learning from dialogs using neural models: Neural networks has been used for response generation that can be trained end to end on large quantities of unstructured Twitter conversations[7]. However this does not incorporate feedback from dialog partner during real time conversation

Reinforcement learning: Reinforcement learning works on dialogs[8][9], often consider reward as the feedback model rather than exploiting the dialog feedback per se.

Forward prediction models: Although forward prediction models, have been used in other applications like learning eye-tracking[10], controlling robot arms[11] and vehicles[12], it has not been used for dialog.

Dialog-based Supervision tasks

For testing their models, the authors chose two datasets (i) the single supporting fact problem from the bAbI datasets [13] which consists of short stories from a simulated world followed by questions; and (ii) the MovieQA dataset [14] which is a large-scale dataset (∼ 100k questions over ∼ 75k entities) based on questions with answers in the open movie database (OMDb)

However, since these datasets were not designed to model the supervision from dialogs, the authors modified them to create 10 supervision task types on these datasets(Fig 3). These are:

DB F3.png


  • Task 1: Imitating an Expert Student: The dialogs take place between a teacher and an expert student who gives semantically coherent answers. Hence, the task is for the learner to imitate that expert student, and become an expert themselves
  • Task 2: Positive and Negative Feedback: When the learner answers a question the teacher then replies with either positive or negative feedback. In the experiments the subsequent responses are variants of “No, that’s incorrect” or “Yes, that’s right”. In the datasets there are 6 templates for positive feedback and 6 templates for negative feedback, e.g. ”Sorry, that’s not it.”, ”Wrong”, etc. To distinguish the notion of positive from negative, an additional external reward signal that is not part of the text
  • Task 3: Answers Supplied by Teacher: The teacher gives positive and negative feedback as in Task 2, however when the learner’s answer is incorrect, the teacher also responds with the correction. For example if “where is Mary?” is answered with the incorrect answer “bedroom” the teacher responds “No, the answer is kitchen”’
  • Task 4: Hints Supplied by Teacher: The corrections provided by the teacher do not provide the exact answer as in Task 3, but only a useful hint. This setting is meant to mimic the real life occurrence of being provided only partial information about what you did wrong.
  • Task 5: Supporting Facts Supplied by Teacher: Another way of providing partial supervision for an incorrect answer is explored. Here, the teacher gives a reason (explanation) why the answer is wrong by referring to a known fact that supports the true answer that the incorrect answer may contradict.
  • Task 6: Partial Feedback: External rewards are only given some of (50% of) the time for correct answers, the setting is otherwise identical to Task 3. This attempts to mimic the realistic situation of some learning being more closely supervised (a teacher rewarding you for getting some answers right) whereas other dialogs have less supervision (no external rewards). The task attempts to assess the impact of such partial supervision.
  • Task 7: No Feedback: External rewards are not given at all, only text, but is otherwise identical to Tasks 3 and 6. This task explores whether it is actually possible to learn how to answer at all in such a setting.
  • Task 8: Imitation and Feedback Mixture: Combines Tasks 1 and 2. The goal is to see if a learner can learn successfully from both forms of supervision at once. This mimics a child both observing pairs of experts talking (Task 1) while also trying to talk (Task 2).
  • Task 9: Asking For Corrections: The learner will ask questions to the teacher about what it has done wrong. Task 9 tests one of the most simple instances, where asking “Can you help me?” when wrong obtains from the teacher the correct answer.
  • Task 10: Asking for Supporting Facts: A less direct form of supervision for the learner after asking for help is to receive a hint rather than the correct answer, such as “A relevant fact is John moved to the bathroom” when asking “Can you help me?”. This is thus related to the supervision in Task 5 except the learner must request help
F4.png


For each task a fixed policy is considered for performing actions (answering questions) which gets questions correct with probability πacc (i.e. the chance of getting the red text correct in Figs. 3 and 4). We thus can compare different learning algorithms for each task over different values of πacc (0.5, 0.1 and 0.01). In all cases a training, validation and test set is provided.

Learning models

This work evaluates four possible learning strategies for each of the 10 tasks: imitation learning, reward-based imitation, forward prediction, and a combination of reward-based imitation and forward prediction

All of these approaches with the same model architecture: an end-to-end memory network (MemN2N) [20], which has been used as a baseline model for exploring differing modes of learning.

F5.png


The input is the last utterance of the dialog, x, as well as a set of memories (context) (c1, . . . , cN ) which can encode both short-term memory, e.g. recent previous utterances and replies, and long-term memories, e.g. facts that could be useful for answering questions. The context inputs ci are converted into vectors mi via embeddings and are stored in the memory. The goal is to produce an output aˆ by processing the input x and using that to address and read from the memory, m, possibly multiple times, in order to form a coherent reply. In the figure the memory is read twice, which is termed multiple “hops” of attention.

In the first step, the input x is embedded using a matrix A of size d × V where d is the embedding dimension and V is the size of the vocabulary, giving q = Ax, where the input x is as a bag-of words vector. Each memory ci is embedded using the same matrix, giving mi = Aci . The output of addressing and then reading from memory in the first hop is:

eq1.png

Here, the match between the input and the memories is computed by taking the inner product followed by a softmax, yielding p 1 , giving a probability vector over the memories. The goal is to select memories relevant to the last utterance x, i.e. the most relevant have large values of p 1 i . The output memory representation o1 is then constructed using the weighted sum of memories, i.e. weighted by p 1 . The memory output is then added to the original input, u1 = R1(o1 + q), to form the new state of the controller, where R1 is a d × d rotation matrix2 . The attention over the memory can then be repeated using u1 instead of q as the addressing vector, yielding:

eq2.png

The controller state is updated again with u2 = R2(o2 + u1), where R2 is another d × d matrix to be learnt. In a two-hop model the final output is then defined as:

eq3.png

where there are C candidate answers in y. In our experiments C is the set of actions that occur in the training set for the bAbI tasks, and for MovieQA it is the set of words retrieved from the KB.

Training strategies

  • 1. Imitation Learning

This approach involves simply imitating one of the speakers in observed dialogs. Examples arrive as (x, c, a) triples, where a is (assumed to be) a good response to the last utterance x given context c. Here, the whole memory network model defined above is trained using stochastic gradient descent by minimizing a standard cross-entropy loss between aˆ and the label a

  • 2. Reward-based Imitation

If some actions are poor choices, then one does not want to repeat them, that is we shouldn’t treat them as a supervised objective. Here, positive reward is only obtained immediately after (some of) the correct actions, or else is zero. Only apply imitation learning on the rewarded actions. The rest of the actions are simply discarded from the training set.

  • 3. Forward Prediction

The aim is, given an utterance x from speaker 1 and an answer a by speaker 2 (i.e., the learner), to predict x¯, the response to the answer from speaker 1. That is, in general to predict the changed state of the world after action a, which in this case involves the new utterance x¯.

F6.png


As shown in figure This is achieved by chopping off the final output from the original network of Fig. 3 (a) and replace it with some additional layers that compute the forward prediction. The first part of the network remains exactly the same and only has access to input x and context c, just as before. The computation up to u2 = R2(o2 + u1) is thus exactly the same as before.

Then perform another “hop” of attention but over the candidate answers rather than the memories. The information of which action (candidate) was actually selected in the dialog (i.e. which one is a) is also incorporated which is crucial. After this “hop”, the resulting state of the controller is then used to do the forward prediction.

Concretely, we compute:

eq4.png

where β ∗ is a d-dimensional vector, that is also learnt, that represents in the output o3 the action that was actually selected. After obtaining o3, the forward prediction is then computed as:

eq5.png

where u3 = R3(o3 + u2). That is, it computes the scores of the possible responses to the answer a over C¯ possible candidates. T


  • 4. Reward-based Imitation + Forward Prediction

As our reward-based imitation learning uses the architecture of Fig. 3 (a), and forward prediction uses the same architecture but with the additional layers of Fig 3 (b), we can learn jointly with both strategies. The former makes use of the reward signal – which when available is a very useful signal – but fails to use potential supervision feedback in the subsequent utterances, as described above. It also effectively ignores dialogs carrying no reward. Forward prediction in contrast makes use of dialog-based feedback and can train without any reward. On the other hand not using rewards when available is a serious handicap. Hence, the mixture of both strategies is a potentially powerful combination.

Experiments

We conducted experiments on the datasets described in Section 3. As described before, for each task we consider a fixed policy for performing actions (answering questions) which gets questions correct with probability πacc. We can thus compare the different training strategies described in Sec. 4 over each task for different values of πacc. Hyperparameters for all methods are optimized on the validation sets.

DB F7.png


The following results are observed:

  • Imitation learning, ignoring rewards, is a poor learning strategy when imitating inaccurate answers, e.g. for πacc < 0.5. For imitating an expert however (Task 1) it is hard to beat.
  • Reward-based imitation (RBI) performs better when rewards are available, particularly in Table 1, but also degrades when they are too sparse e.g. for πacc = 0.01.
  • Forward prediction (FP) is more robust and has stable performance at different levels of πacc. However as it only predicts answers implicitly and does not make use of rewards it is outperformed by *RBI on several tasks, notably Tasks 1 and 8 (because it cannot do supervised learning) and Task 2 (because it does not take advantage of positive rewards).
  • FP makes use of dialog feedback in Tasks 3-5 whereas RBI does not. This explains why FP does better with useful feedback (Tasks 3-5) than without (Task 2), whereas RBI cannot.
  • Supplying full answers (Task 3) is more useful than hints (Task 4) but hints still help FP more than just yes/no answers without extra information (Task 2).
  • When positive feedback is sometimes missing (Task 6) RBI suffers especially in Table 1. FP does not as it does not use this feedback.
  • One of the most surprising results of our experiments is that FP performs well overall, given that it does not use feedback, which we will attempt to explain subsequently. This is particularly evident *on Task 7 (no feedback) where RBI has no hope of succeeding as it has no positive examples. FP on the other hand learns adequately.
  • Tasks 9 and 10 are harder for FP as the question is not immediately before the feedback.
  • Combining RBI and FP ameliorates the failings of each, yielding the best overall results

Conclusion and future work

This paper only studies some restricted types of feedback, namely positive feedback and corrections of various types. However, potentially any reply in a dialog can be seen as feedback, and should be useful for learning. It should be studied if forward prediction, and the other approaches we tried, work there too. Future work should also develop further evaluation methodologies to test how the models we presented here, and new ones, work in those settings, e.g. in more complex settings where actions that are made lead to long-term changes in the environment and delayed rewards, i.e. extending to the reinforcement learning setting, and to full language generation. Finally, dialog-based feedback could also be used as a medium to learn non-dialog based skills, e.g. natural language dialog for completing visual or physical tasks.

References

  1. Jason Weston. Dialog-based Language Learning. NIPS, 2016.
  2. P. K. Kuhl. Early language acquisition: cracking the speech code. Nature reviews neuroscience, 5(11): 831–843, 2004.
  3. M. A. Bassiri. Interactional feedback and the impact of attitude and motivation on noticing l2 form. English Language and Literature Studies, 1(2):61, 2011.
  4. R. Higgins, P. Hartley, and A. Skelton. The conscientious consumer: Reconsidering the role of assessment feedback in student learning. Studies in higher education, 27(1):53–64, 2002.
  5. A. S. Latham. Learning through feedback. Educational Leadership, 54(8):86–87, 1997.
  6. M. G. Werts, M. Wolery, A. Holcombe, and D. L. Gast. Instructive feedback: Review of parameters and effects. Journal of Behavioral Education, 5(1):55–75, 1995.
  7. A. Sordoni, M. Galley, M. Auli, C. Brockett, Y. Ji, M. Mitchell, J.-Y. Nie, J. Gao, and B. Dolan. A neural network approach to context-sensitive generation of conversational responses. Proceedings of NAACL, 2015.
  8. V. Rieser and O. Lemon. Reinforcement learning for adaptive dialogue systems: a data-driven methodology for dialogue management and natural language generation. Springer Science & Business Media, 2011.
  9. J. Schatzmann, K. Weilhammer, M. Stuttle, and S. Young. A survey of statistical user simulation techniques for reinforcement-learning of dialogue management strategies. The knowledge engineering review, 21(02):97–126, 2006.
  10. J. Schmidhuber and R. Huber. Learning to generate artificial fovea trajectories for target detection. International Journal of Neural Systems, 2(01n02):125–134, 1991.
  11. I. Lenz, R. Knepper, and A. Saxena. Deepmpc: Learning deep latent features for model predictive control. In Robotics Science and Systems (RSS), 2015.
  12. G. Wayne and L. Abbott. Hierarchical control using networks trained with higher-level forward models. Neural computation, 2014.
  13. B. C. Stadie, S. Levine, and P. Abbeel. Incentivizing exploration in reinforcement learning with deep predictive models. arXiv preprint arXiv:1507.00814, 2015.
  14. J. Clarke, D. Goldwasser, M.-W. Chang, and D. Roth. Driving semantic parsing from the world’s response. In Proceedings of the fourteenth conference on computational natural language learning, pages 18–27. Association for Computational Linguistics, 2010.
  15. J. Schatzmann, K. Weilhammer, M. Stuttle, and S. Young. A survey of statistical user simulation techniques for reinforcement-learning of dialogue management strategies. The knowledge engineering review, 21(02):97–126, 2006.