Paper:
We introduce a novel framework for evaluating multimodal deep learning models with respect to their language understanding and generalization abilities. In this approach, artificial data is automatically generated according to the experimenter’s specifications. The content of the data, both during training and evaluation, can be controlled in detail, which enables tasks to be created that require generalization abilities, in particular the combination of previously introduced concepts in novel ways. We demonstrate the potential of our methodology by evaluating a multimodal architecture on four different tasks, and show that our framework gives us insights into the model’s capabilities and limitations.
1 Introduction
Deep learning methods have had a major impact on research in natural language processing and raised performance substantially in many of the standard evaluations. Moreover, multimodal tasks like image captioning (Karpathy and Li, 2015) or visual question answering (Antol et al., 2015) can now be tackled with great success. Such systems seem to solve the problems entirely on a sub-symbolic level, based only on raw image (and text) input, whereas previous approaches required a hand-crafted combination of various higher-level components.
There is, however, concern about how deep neural networks learn to solve such tasks. Investigations for image recognition (Szegedy et al., 2014; Nguyen et al., 2015; Zhang et al., 2017) have shown surprising behavior very different from what would be expected of their “surpassing
human-level performance” (He et al., 2015). Deep networks for language tasks may exhibit similarly odd behavior (Sproat and Jaitly, 2016; Arthur et al., 2016). Such results cast doubt on whether deep learning systems reliably acquire appropriate generalizations. However, given the recursive nature of language and the potentially enormous problem space of image captioning and similar tasks, acquiring the ability for reliable generalization will eventually be essential.
A more theoretical issue is the capability of network architectures to be able, in principle, to learn certain classes of structure. For instance, it has been shown that LSTMs possess the ability to handle long-range dependencies (Hochreiter and Schmidhuber, 1997; Gers and Schmidhuber, 2001). However, the formal experiments that have been done along such lines are limited, particularly in the multimodal domain of vision and language. While recent work indicates that the information encoded in image embeddings is rich enough for good captioning results, it is an open question whether current multimodal architectures are able, in principle, to combine visual information effectively to handle the full range of linguistic constructions.
This paper introduces a new test methodology for multimodal deep learning models. SHAPEWORLD is a framework for specifying datasets, which differ from standard evaluation datasets in two main ways. Firstly, a SHAPEWORLD dataset defines a process for generating artificial data, which is randomly sampled during training/testing according to constraints specified by the experimenter. Secondly, the evaluation focus of the methodology is on linguistic understanding capabilities of the type investigated by formal semantics. Hence the visual complexity and the openclass vocabulary size is reduced to a minimum, while allowing indefinitely complex syntactic con-
2
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
structions. Since it is possible to control data generation for both training and evaluation, we can introduce previously unseen configurations in the test data. This allows us to design tasks which require the system to recombine learned concepts to understand these novel instances, and is hence a form of zero-shot learning. We think of the SHAPEWORLD tasks as unit-testing multimodal systems for specific linguistic generalization capabilities, in a similar way to the bAbI task set of Weston et al. (2015) for text-only understanding.
We also present initial results on the performance of a generic multimodal architecture. The interest here is not obtaining high numbers, but rather analyzing the performance of a generic model with our evaluation methodology and demonstrating the potential of SHAPEWORLD in investigating multimodal deep neural networks. In fact, we have been surprised to see some poor performance even for simple tasks. We invite the community to explore the SHAPEWORLD tasks and use them to evaluate other successful image captioning or visual question answering systems. We want to emphasize, however, that the goal is not to achieve optimal performance by tuning a network architecture for one of these tasks. The central question is rather whether deep network architectures are able to successfully demonstrate the required understanding and generalization ability, and whether this will carry over to more complex tasks which can be defined using SHAPEWORLD.
2 Related work
With the increasing popularity of deep learning approaches, artificial data of various kinds is again seen as a valuable tool in experimentation. Recently, the simulation paradigm has been argued to be a promising driver for artificial intelligence research (Kiela et al., 2016). Various platforms following this paradigm have already been released, amongst others, OpenAI Gym (Brockman et al., 2016), DeepMind Lab (Beattie et al., 2016), Project Malmo (Johnson et al., 2016), to name a few of the most popular. An important advantage of simulated data is its infinite availability, particularly in light of the need of many deep learning models for huge amounts of data. Automatically generating data greatly reduces the cost, time and human effort. Moreover, it allows researchers to focus on particular problems, isolated from noisy
and complex environments. When focusing on language tasks, the simulation paradigm faces the problem that interesting language generation is a difficult task in its own right and that the difficulty increases with the complexity of the underlying world. The bAbI tasks (Weston et al., 2015) are generated by internally simulating a short scene and extracting a few simple sentences from it. A similar approach is taken by Narasimhan et al. (2015), but here the simulation is more complex, comprising a text-based role-playing game. The MazeBase game environment (Sukhbaatar et al., 2015) also uses language as a mean to represent the game world. However, the descriptions are in an abstract, formulaic format, and the focus of the simulation is much more on the planning than the language component. The long-term research proposal of Mikolov et al. (2015) also simulates a world where an agent learns to solve tasks by communication with a teacher module. At least for a start, this module is supposed to be scripted to automatically generate appropriate responses, given its internal knowledge of the world state.
Automatically generated data is common for tasks specifically focusing on the ability to efficiently process data of a certain formal structure. Here, data is deliberately stripped of any realworld connection to create an abstract capability check. Recent work in the context of deep learning has investigated sequence patterns (Joulin and Mikolov, 2015), combinatorial problems (Vinyals et al., 2015), or executing programming language code (Zaremba and Sutskever, 2014), amongst others. This kind of tasks is particularly common for neural network models (compare e.g., Bengio et al. (1994) more than twenty years ago). The reason for interest in abstract capability tests is that the learning process of deep neural networks is far more difficult to understand in a detailed way than shallower machine learning methods.
The multimodal tasks of image captioning and visual question answering are most closely related to our methodology, but usually consist of repurposed real-world photos and human-written descriptions1. However, there have been experiments in which parts of the data are artificial or generated automatically: for instance, automatic
1Although we use the term “real-world”, in contrast to artificial simulations, it should be clear that it is very unlike the visual input which would be experienced by an entity situated in the real world
3
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
question generation (Ren et al., 2015) or modification of captions (Hodosh and Hockenmaier, 2016). Abstract Clipart scenes have been used for image captioning (Zitnick et al., 2016; Zitnick and Parikh, 2013). Most similar to our work are the experiments done by Bowman et al. (2015) and Sorodoc et al. (2016). Both generate artificial data fully automatically, based on abstract models, for a task which is targeted at a specific linguistic aspect, logical semantics and quantifiers, respectively.
Our own work is based on automatically generated, fully artificial data. This data, however, is not designed to address only a single structural problem, but is able to cover a whole range of linguistic phenomena. In fact, our generation system closely resembles classical work in formal semantics, where a statement corresponds to a logical expression which can be evaluated against an abstract world model (Montague, 1970). We utilize broad-coverage realization driven by the English Resource Grammar (ERG) (Flickinger, 2000) processed with Packard’s Answer Constraint Engine (ACE, http://sweaglesw. org/linguistics/ace/). However, while SHAPEWORLD uses a logical representation internally, the external representation seen by the system under evaluation does not involve any abstract formalization of text or images. It nevertheless presents the intended problems clearly, without any uncontrolled noise, biases, or potentially hidden correlations, which can obfuscate results when using real-world images and text.
3 The SHAPEWORLD framework
The SHAPEWORLD framework2 is based on the concept of microworlds – small and selfcontained artificial scenarios – which guide the data creation process. The SHAPEWORLD microworlds simply consist of colored shapes. This closed-world domain allows for exhaustive coverage of the space of possible microworlds and associated captions, reducing the vocabulary size to (for now) far less than 100 words with an emphasis
2 The SHAPEWORLD code is written in Python 3 and is available on GitHub (https://git.io/vDWOX). The generated data is returned as NumPy arrays, so that it is possible to integrate it into Python-based deep learning projects based on common frameworks like TensorFlow, Theano, etc. In our experiments, we use Tensorflow and we provide example scripts as part of the package. The Python package pydmrs (Copestake et al., 2016) is required for the internal DMRS-based caption generation.
on closed-class words. In the following we explain the details of the data generation process inside the SHAPEWORLD framework. A schematic illustration of the process is shown in figure 1.
3.1 Image caption agreement task
In this paper we focus on the task of image caption agreement. The system to be evaluated is presented with an image and a natural language caption, and has to decide whether they are consistent with each other. Compared to the classic image captioning task, it emphasizes understanding rather than the synthesis part of language use. We therefore avoid the problem of evaluating the appropriateness of a caption. The setup allows us to control the content of both modalities and consequently force a system to cope with difficult types of captions while obtaining a clear indicator of successful understanding.
One further motivation for the task is that human performance could be measured using the same setup. We would expect close-to-perfect human performance on the tasks described here, assuming time is not tightly constrained. Interesting comparisons are potentially possible where human performance depends on presentation: e.g., quantifiers such as most (Pietroski et al., 2009). However, we will not discuss this further in the current paper.
3.2 World and image generation
At the core of each abstract microworld instance lies an abstract world model. In the SHAPEWORLD framework, the internal representation of a microworld is simply a list of entities given as records containing their primary attributes, such as position, shape, color, which are considered to be high-level semantic aspects reflected in captions. In addition, an entity has secondary attributes and methods which control, for instance, details of visual appearance, visual noise infusion, or the collision-free placement of entities.
Currently, the SHAPEWORLD framework provides eight shape types (square, rectangle, triangle, pentagon, cross, circle, semicircle and ellipse) and seven colors (red, green, blue, yellow, magenta, cyan and white). The location and the secondary attributes are sampled uniformly or according to a truncated normal distribution (object size: 10% to 15% of image size; rotation: random; shade: shifted up to 50% towards black or white; and general noise: up to 10% deviation
4
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
World Generator World Captioner
World Model {s : tr, c : gr, x : 0.9, y : 0.1, . . . }, {s : ci, c : bl, x : 0.2, y : 0.3, . . . }, {s : cr, c : re, x : 0.7, y : 0.5, . . . }, {s : sq, c : re, x : 0.8, y : 0.8, . . . }, {s : ci, c : gr, x : 0.3, y : 0.9, . . . }
Model for Caption {s : cr, c : bl, x : 0.9, y : 0.1, . . . }, {s : tr, c : re, x : 0.2, y : 0.3, . . . }, {s : cr, c : re, x : 0.7, y : 0.5, . . . }, {s : sq, c : gr, x : 0.8, y : 0.8, . . . }
DMRS Graph Pattern
Caption Object Image:
Agreement: False ≈ 0 ∈ [0, 1]
Caption: There is a red triangle.
(for negative instance)
(for positive
instance)
Figure 1: The generation process for image caption agreement data. Depending on whether a positive instance, i.e., a true statement about the world, or a negative instance is to be generated, either the first world model is used for caption generation or a new microworld is generated.
per pixel). Importantly, all these ways of infusing noise can be controlled, which is useful particularly since noise is often seen as important for successful training of deep models.
The generator module automatically generates a world model by randomly sampling all these attributes from a set of available values. Both the values and other aspects of the generation process can be specified and adjusted appropriately. The internal abstract representation is then used as a basis to extract a concrete microworld instance consisting of image and caption. An image (of size 64×64 in this work) is just a straightforward visualization of the world model. Note that the exact visual appearance of an entity with certain primary attributes varies from instance to instance. Caption generation is more complex, as discussed in the next section.
3.3 Caption generation
The caption interface specifies the methods a language generation module has to provide to integrate into the data generation process. We currently provide an implementation using a grammar-based approach. More specifically, Dependency Minimal Recursion Semantics (DMRS) is an abstract semantic graph representation designed for use with high-precision grammars, such as those distributed by the DELPH-IN consortium. A semantic representation like DMRS is particularly suited for the SHAPEWORLD framework, since it essentially mirrors parts of the internal world model and hence acts like a languagespecific annotation.3 For instance, noun nodes
3Although we currently use the English Resource Grammar (Flickinger, 2000), other DELPH-IN grammars use a
correspond to entities, adjective nodes add attributes, and verb phrase nodes or sub-graphs specify relations between entities. Below an example of a DMRS semantic graph with its compositional components colored:
There is a blue circle.
Compositionality of the semantic representation is a useful property and an important reason for our choice of using DMRS. Given compositionality, it is enough to specify the semantics of words – or, more precisely, of the linguistic atoms in the SHAPEWORLD context, which potentially are sub-graphs with multiple nodes and inner link structure – to be able to obtain the corresponding semantics of composed sub-graphs and so generate a wide range of combinatorially different captions. For instance, the semantics of words like square or red iteratively filter a subset of agreeing entities, transitive relations like to the left of act similarly on pairs of entity sets, and quantifiers compare the cardinality of two entity sets.
Figure 2 shows an example of a more complex compositional caption, which contains the DMRS graph above as sub-graph. It also illustrates how various details are automatically inferred by the ERG, including number-agreement between subject and verb, and between quantifier and noun, and realization of an adjective as relative clause.
compatible approach, so SHAPEWORLD could be ported to other languages relatively easily.
5
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
Most squares are green and there are some circles which are blue.
≈ [ #{s1∈World : square(s1.shape)∧ green(s1.color)}
#{s2∈World : square(s2.shape)} > 1 2
] ∧ [ ∃s3 ∈ World : circle(s3.shape) ∧ blue(s3.color) ]
Figure 2: An example of a DMRS graph corresponding to a more complex caption, with compositional components colored. The logical formula gives the formal semantic interpretation over a world model.
This greatly facilitates the generation of a combinatorially large amount of captions and makes the DMRS graph patterns reusable, as outlined in section 4. Figure 2 furthermore gives a formal semantic interpretation of the caption meaning as logical formula over a world model. In essentially the same way, the agreement of a caption with a microworld is computed in the SHAPEWORLD framework.
Similar to the generator module, the captioner module also randomly samples from a set of dataset-specific DMRS graph patterns, which are then applied to a world model to construct an agreeing caption object. Such a caption object can be turned into natural language. Its ability to check whether another world model would agree with the semantics of this caption is important for the generation of negative instances, i.e., captions that do not agree with the corresponding microworld they are supposed to describe. These captions are obtained by sampling a second, false world model, extracting a caption object from it, and ensuring that it does not accidentally also agree with the first, true microworld.
3.4 Training and testing on SHAPEWORLD datasets
Since SHAPEWORLD datasets are actually data generation processes, training and evaluation work differently from classic datasets. Where usually one has a fixed set of test instances, here models are trained and tested on a fixed set of more abstract configuration constraints. In particular, in our experiments, the constraints for testing (and validation) differ from the training constraints, hence requiring true generalization abilities. This means that, for instance, a certain shapecolor-combination, a specific number of objects, a spatial location or a caption type can be held-out and never generated during training. The model is
presented with instances of this configuration only when evaluated and is hence required to recombine concepts it acquired from the training data. It is thus possible for a system to achieve optimal performance during training, but completely fail the evaluation. Another parameter is the ratio of correct instances, which may differ between training and evaluation. This adds yet another level of control to allow us to analyze the behavior of a model.
4 Some SHAPEWORLD datasets
In this paper we look at four datasets, each designed to investigate an aspect of the capability to understand language in a multimodal setup and to generalize to new instances not seen during training. Two example images of microworlds for each dataset are shown in Figure 3.
OneShape The first and simplest dataset requires the evaluated system to learn to separate the concepts of shape and color, instead of treating each combination as an atomic object. For this, microworlds are generated which contain a single object, together with a simple existential statement of the form: There is a [color] [shape]. One color-shape-combination, red square, is reserved for evaluation, which leaves 55 combinations for training.
MultiShape This dataset generates worlds with up to four objects, with the same type of existential statement as before, describing one of these objects. The system is evaluated on worlds containing five objects, requiring the ability to focus attention on one object without being distracted by other objects.
Spatial Microworlds with two objects are generated in this dataset accompanied by a statement about their relative location: to the left of, to the
6
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
ONESHAPE MULTISHAPE SPATIAL QUANTIFIER
Figure 3: Two example microworlds generated by each of the four presented SHAPEWORLD datasets.
right of, above or below. The overall caption is of the form: The [color] [shape] is [spatial] the [color] [shape]. As in the first dataset, a previously unseen attribute combination is presented for evaluation.
Quantifier The quantifier dataset generates microworlds of three to six objects, with seven used for evaluation. The captions focus on the quantifiers no, a, the, some, two, most, all and every. Compatibility with the caption is analyzed according to the quantifier’s usual cardinality-based interpretation in formal semantics. For instance, most is considered to be true if the target attribute applies to more than half of the domain set of objects. There are a variety of possible caption patterns in this dataset with different degrees of underspecification in domain and target, as illustrated in the examples below:
• Some shapes are green. • Most shapes are rectangles. • No shape is a red triangle. • All triangles are green. • Two blue shapes are pentagons.
Since we first sample a microworld model and subsequently a caption, here we cannot easily control the sampling process to uniformly sample each possible caption, as is trivially the case for the other datasets. Consequently, we restrict the number of different shapes and colors possible in a world, so that situations for the quantifiers all, every and most become more likely. Moreover, we adapt the probabilities of the sampling process so that we observe a more uniform distribution over caption patterns when sampling a large number of instances.
An important property of the SHAPEWORLD datasets is their compositionality. Instead of having to define a dataset from scratch every time, we can specify atomic datasets like the ones described above, and then combine them in a mixer dataset, which tests for various different aspects
of multimodal language understanding simultaneously. Reusability in fact applies even further down in the component hierarchy. For instance, we use the same generic world generator module for all four datasets. This is even more important for caption generation where, for instance, a logical combinator dataset can reuse different world captioner modules to generate simple statements which then are merged by logical connectives.
5 Experiments
As discussed in the introduction, the aim of our experiments is to demonstrate that SHAPEWORLD allows detailed investigation of neural network architectures, rather than high performance as such, although naturally a certain minimum performance is required for the results to be interesting.
5.1 Network architecture
We evaluate a generic multimodal deep neural network architecture shown in figure 4, which is inspired by recent work on visual question answering (Antol et al., 2015). The visual input is processed by a convolutional neural network (CNN) module (LeCun et al., 1989) with three layers of convolutions (filter sizes: 5, 3, 3; number of filters: 16, 32, 64) followed by a 2×2 max-pooling step, or average-pooling in the last layer, respectively. With an image size of 64×64, the obtained world embedding has 4096 dimensions. The textual input is transformed to 32-dimensional embeddings and passed on to a long short-term memory (LSTM) module (Hochreiter and Schmidhuber, 1997) with state size 64. The final state is used as the caption embedding, which is scaled and fused with the world embedding via pointwise multiplication. After a fully-connected hidden layer of 512 dimensions, yield the agreement score. The entire architecture is trained end-toend on the task. Both the CNN module and the word embeddings are included in the training, as opposed to using pre-trained, general-purpose versions.
7
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
CNN CNN CNN
world embedding
LSTM LSTM LSTM LSTM LSTM
there is a green circle
caption embedding
word embeddings lookup
?
agreement embedding hidden layer agreement ∈ [0, 1]
scale
Figure 4: A schematic depiction of the multimodal deep neural network which we evaluate on the SHAPEWORLD datasets.
ONESHAPE
0.5
0.6
0.7
0.8
0.9
1
0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000
training evaluation
ONESHAPE (*)
0.5
0.6
0.7
0.8
0.9
1
0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000
training evaluation
MULTISHAPE
0.5
0.6
0.7
0.8
0.9
1
0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000
training evaluation
SPATIAL
0.5
0.6
0.7
0.8
0.9
1
0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000
training evaluation
SPATIAL (*)
0.5
0.6
0.7
0.8
0.9
1
0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000
training evaluation
QUANTIFIER
0.5
0.6
0.7
0.8
0.9
1
0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000
training evaluation
Figure 5: Performance of the evaluated multimodal deep neural network on the four SHAPEWORLD datasets, measured as accuracy of correctly judged image caption agreements. .
5.2 Training and evaluation setup
In all experiments, we use a training batch size of 128 where the ratio of correct instances is 33%. To track the performance of the model throughout the learning process (as opposed to just at the end), we add an evaluation step every 100th training iteration, where we obtain the accuracy on both training and evaluation instances. These numbers are calculated on basis of a batch of 1024 instances each, with a correct instance ratio of 50%, and are used to visualize and investigate the learning performance in our experiments.
5.3 Results
Figure 4 shows the performance results of our experiments. We observe a high accuracy on the ONESHAPE dataset on training instances, but an unexpectedly low and unstable performance on the
held-back instances. This indicates that the system does not naturally learn to distinguish shape and color as two separate concepts, but rather learns to classify each shape-colour-combination individually – after all, the ONESHAPE dataset essentially resembles a classification task. In a followup experiment, we reduced the embedding size to 8 and the LSTM state size to 16 dimensions (marked with (*) in figure 4). Being given less memory to learn simple non-generalizing classification, the model indeed achieves similarly high accuracy numbers in evaluation as during training.
The training performance on the MULTISHAPE dataset is relatively low and plateaus at around 80-85% after roughly 5000 iterations. The evaluation accuracy, in comparison, is not that much lower, indicating that the generalization to an unseen number of objects is learned, to some degree.
8
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
On the SPATIAL dataset, unlike on ONESHAPE, the system successfully learns the shape-colordistinction from the start. Note that each caption here involves two object descriptions, so instead of simply learning classification, it is necessary to disentangle the words of the caption. The fact that the evaluation accuracy is higher than the training accuracy in the beginning is because the held-back combination red square is (unsurprisingly) easier to recognize than some others. Again, we also ran an experiment with reduced embedding and LSTM size. Contrary to ONESHAPE, the accuracy curves indicate that this impedes the learning of an appropriate generalization ability for the more complex SPATIAL task.
The QUANTIFIER dataset confirms the lower accuracy of captions focusing on parts of a world which contains multiple shapes. The training accuracy is around 5% lower than for MULTISHAPE and shows no substantial difference to the validation accuracy, implying that quantifiers, expectedly, are more difficult to learn than existential statements, but definitely possible to some degree.
5.4 Discussion
We want to emphasize the fact that the training accuracy here represents an interesting measure on its own. Contrary to the common evaluation methodology, where training performance is less expressive after multiple iterations over the training data, each instance is randomly generated anew in our setting. Consequently, even the training accuracy reflects a learning process, and overfitting happens on a conceptual/structural level instead of a numerical correlation level. Investigations of overfitting in this sense are not easily possible with non-artificial data.
Another type of investigation of what a model has learned involves manipulation of the evaluation data. For instance, we analyzed the fact that the network only achieves around 95% accuracy on the supposedly simple ONESHAPE dataset. Looking at a few misclassifications, we found that sometimes the negative instances are problematic where either only the shape or only the color attribute differed from what the caption stated. We thus created a modified version of the ONESHAPE dataset which generates only negative instances of this structure. Applying the trained model to this dataset indeed gave a training accuracy of around 80% and an evaluation accuracy of only 55-60%.
Changing the network parameters altered the performance for the ONESHAPE and SPATIAL datasets in interestingly different ways. In general, when we changed parameters in our experiments, we often observed either no learning at all, less (or no) improvement in evaluation accuracy compared to training accuracy, or similar curves for both numbers. Given our aim to set up capability unit-tests, such discrete, to some degree invariant outcomes are what we ideally expect – a behavior we intend to investigate further. Another aspect, which might turn out interesting in more complex experiments is the shape of the accuracy curves.
6 Conclusion and future work
We have presented a new test methodology and framework, SHAPEWORLD, for multimodal deep learning models with a focus on formal-semantic style generalization capabilities. In this framework, artificial data is automatically generated, and datasets specify parameters of this generation process to create appropriate data, which targets a specific multimodal understanding task. We evaluated a neural network architecture on four image caption agreement datasets, where the system has to decide whether a statement applies to an image. We show how the SHAPEWORLD framework can be used to investigate the learning process of deep neural networks, and that it clearly indicates whether certain abilities are acquired. There are clearly some deficiencies in the generalization ability of the evaluated network – one aim of this paper is to invite the research community to evaluate their architectures on our datasets.
The SHAPEWORLD framework is still under development. In particular, we plan to add new datasets addressing other aspects of language, as well as integrating options to enhance the language generation module, with the aim of providing more varied and natural image descriptions. One option would be to integrate a subsequent step applying paraphrase rules after caption generation – Copestake et al. (2016) describe how this can be implemented on the level of DMRS graphs. In addition, the framework can easily be extended to produce more complex worlds, e.g., based on Cliparts (Zitnick et al., 2016). However, we plan to stick to the simplicity and abstractness of our microworlds for now, since we think they offer several advantages and enough richness for various interesting investigations.
9
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899

Review:
- Strengths:  The authors introduce a new software package called ShapeWorld for automatically generating data for image captioning problems. The microworld used to generate the image captions is simple enough to make the data being generated and errors by a model readily interpretable. However, the authors demonstrate that configurations of the packages produce data that is challenging enough to serve as a good benchmark for ongoing research.  - Weaknesses:  The primary weakness of this paper is that it does look a bit like a demo paper. The authors do provides experiments that evaluate a reasonable baseline image captioning system on the data generated by ShapeWorld. However, similar experiments are included in demo papers.  The paper includes a hyperlink to the software package on github that presumably unmasks the authors of the paper.  - General Discussion:  Scientific progress often involves some something analogous to vygotsky's zone of proximal development, whereby progress can be made more quickly if research focuses on problems with just the right level of difficulty (e.g., the use of tidigits for speech recognition research in the early 90s). This paper is exciting since it offers a simple microworld that is easy for researchers to completely comprehend but that also is just difficult enough for existing models.  The strengths of the work are multiplied by the fact that the software is opensource, is readily available on github and generates the data in a format that can be easily used with models built using modern deep learning libraries (e.g., TensorFlow).  The methods used by the software package to generate the artificial data are clearly explained. It is also great that the authors did experiments with different configurations of their software and a baseline image caption model in order to demonstrate the strengths and weakness of existing techniques.   My only real concern with this paper is whether the community would be better served by placing it in the demo section. Publishing it in the non-demo long paper track might cause confusion as well as be unfair to authors who correctly submitted similar papers to the ACL demo track.