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ListOps: A Diagnostic Dataset for Latent Tree Learning

Nikita Nangia, Samuel R. Bowman

arXiv:1804.06028v1cs.CL

TL;DR

Latent tree models are difficult to evaluate because natural language often permits multiple valid parses. The paper introduces ListOps, a diagnostic toy dataset, and finds that leading latent tree models fail to learn its required parsing strategy, performing worse than sequential RNNs.

  • Problem

    Natural language often permits multiple valid parses, making latent tree models’ parsing ability difficult to study.

  • Method

    The paper introduces ListOps, a prefix-arithmetic toy dataset of deeply nested operations designed to make correct parsing essential to success.

  • Results

    Leading latent tree models fail to learn to parse ListOps, achieving 64.8% and 61.0% accuracy versus 98.7% for a TreeLSTM with ground-truth parses.

  • Takeaways & Limitations

    ListOps provides a diagnostic test indicating whether latent tree models can learn a sensible parsing strategy when parsing is essential.

  • Takeaways & Limitations

    ListOps’s diagnostic value depends on maintaining a large RNN–TreeRNN performance gap, although sufficiently capable RNNs could solve it in principle.

Abstract

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Latent tree learning models learn to parse a sentence without syntactic supervision, and use that parse to build the sentence representation. Existing work on such models has shown that, while they perform well on tasks like sentence classification, they do not learn grammars that conform to any plausible semantic or syntactic formalism (Williams et al., 2018a). Studying the parsing ability of such models in natural language can be challenging due to the inherent complexities of natural language, like having several valid parses for a single sentence. In this paper we introduce ListOps, a toy dataset created to study the parsing ability of latent tree models. ListOps sequences are in the style of prefix arithmetic. The dataset is designed to have a single correct parsing strategy that a system needs to learn to succeed at the task. We show that the current leading latent tree models are unable to learn to parse and succeed at ListOps. These models achieve accuracies worse than purely sequential RNNs.

1 Introduction

Latent tree models can perform well on sentence understanding without syntactic supervision, yet may learn implausible and inconsistent grammars. ListOps addresses this diagnostic challenge with a toy prefix-arithmetic dataset designed to isolate parsing ability through a single reference parsing strategy.

  • Motivation: Latent tree models learn parses indirectly from downstream semantic tasks rather than ground-truth syntactic parses.These models use tree-structured recurrent networks without direct syntactic supervision and have performed well on sentence understanding tasks.
  • Motivation: Current latent tree models do not reliably learn recognizable semantic or syntactic grammars, and their learned grammars vary across random restarts.This evidence comes from models trained on natural language inference.
  • Motivation: Natural language makes parsing ability difficult to study because sentences can have multiple valid parses and sensible grammars are not always needed for task success.The authors therefore seek a controlled setting that primarily tests whether a system can learn a correct parsing strategy.
  • ListOps: ListOps is a toy dataset of deeply nested prefix-arithmetic sequences designed to primarily test parsing ability.Its sequences are generated with a reference parse corresponding to the simplest available interpretation strategy.
  • Contribution: ListOps becomes dramatically harder without reference parses, making it a litmus test and diagnostic tool for latent tree learning models.Given ground-truth parses, succeeding is described as trivially easy; without them, success depends on learning the parsing strategy.

2 Related Work

Prior latent tree learning work has studied natural-language tasks using TreeLSTMs and distant supervision, with parsers differing in how they make discrete or soft structural decisions. This work instead uses a toy dataset to isolate parsing ability, motivated by prior toy-task research in NLP.

  • Prior latent tree models: Existing latent tree studies evaluate models in natural-language settings, including textual entailment on the SNLI and MultiNLI corpora.Williams et al. (2018a) analyze RL-SPINN and ST-Gumbel, two leading latent tree models.
  • Prior latent tree models: All three models use TreeLSTMs and learn parses through distant supervision from a downstream semantic objective.RL-SPINN trains its parser with REINFORCE because its parser makes discrete decisions that cannot be trained with backpropagation.
  • Prior latent tree models: Maillard et al.’s model uses a CYK-style chart parser to softly combine all valid binary trees, requiring O(N^2) possible tree nodes for N words.This computational cost is especially problematic for ListOps because its sequences are very long.
  • Toy datasets: This work studies latent tree parsing with a toy dataset, following earlier NLP research that used controlled tasks such as bAbI to examine system performance.The bAbI tasks were designed as prerequisites for systems aiming to succeed at language understanding.

3 Dataset

ListOps is a balanced ten-way classification dataset of prefix-notation list operations designed to isolate latent parsing ability. Ground-truth parses make it easy, whereas nested sequences require sequential models to retain information about many open lists.

  • Description: ListOps maps prefix-notation operations over single-digit integers to a single-digit solution, forming a balanced ten-way classification task.Each operation has a closing bracket defining its list; for example, [MAX 2 9 [MIN 4 7 ] 0 ] yields 9.
  • Description: The dataset uses four simple operations: MAX, MIN, MED, and sum modulo 10 (SM).The operation space is intentionally small to reduce output-set difficulty and focus evaluation on parsing rather than mathematical approximation.
  • Efficacy: ListOps is easy with ground-truth parses but can require a model without parses to maintain a large stack, especially for long sequences with nested lists.Each operation can be computed with constant memory, but a sequential model must retain the state of every currently open operation.
  • Efficacy: Tree-structured models need only small per-node constituent information, whereas fixed-size sequential models must preserve information across all open lists.This difference explains why TreeRNNs can achieve high accuracy without storage requirements growing with tree depth.
  • Generation: The dataset’s difficulty is controlled primarily by tree depth and operation space, with increasing recursion reducing sequential-model performance.The authors use MAX, MIN, MED, and SM because these easy operations produced larger RNN–TreeRNN gaps than multiplication.
  • Generation: ListOps contains 90k training examples and 10k test examples, with randomly selected operations balanced in the final dataset.A generation script also allows variation in maximum tree depth and the number and types of operations.

4 Models

The study compares sequential, supervised tree-structured, and latent tree learning models on ListOps, using LSTM, TreeLSTM, RL-SPINN, and ST-Gumbel. All models share a PyTorch implementation and are trained with tuned hyperparameters, a two-layer MLP, and a ten-way classifier.

  • Model variants: The model comparison includes an LSTM sequential baseline, a TreeLSTM tree-structured baseline, and two latent tree models: RL-SPINN and ST-Gumbel.The latent tree models follow the names used by Williams et al. (2018a).
  • Training details: All models are implemented in a shared PyTorch 0.3 codebase, with extensive tuning of learning rate, L2 regularization, and learning-rate decay.The code is available on GitHub, and baseline model size is tuned to establish the RNN–TreeRNN gap.
  • Model variants: On ListOps, RL-SPINN and ST-Gumbel perform worse than the LSTM baseline, despite latent tree models outperforming the supervised TreeLSTM on SNLI.Table 1 reports these results on SNLI and ListOps.
  • Training details: Each model passes its representation through a 2-layer MLP and ten-way softmax classifier, using Adam with default beta and epsilon parameters.These architectural and optimization choices apply to all models.

5 ListOps Results

On ListOps, TreeLSTM substantially outperforms sequential LSTM baselines, while latent tree models perform poorly despite outperforming TreeLSTM on SNLI. These results indicate that latent tree models fail to learn the parsing strategy that ListOps rewards.

  • Baseline models: TreeLSTM performs well at 48D, whereas LSTM remains weak at 1024D despite heavy hyperparameter tuning.The authors report a large RNN–TreeRNN gap on ListOps.
  • Baseline models: A ∼25% performance gap between tree-based and sequential baselines makes ListOps a setting for testing latent-tree parsing without syntactic supervision.The dataset strongly rewards learning its correct parsing strategy.
  • Latent tree models: On SNLI, all latent tree models outperform the TreeLSTM baseline, with ST-Gumbel achieving a sizable margin.The same latent tree models perform very poorly on ListOps.
  • Latent tree models: Latent tree models perform substantially worse than the LSTM baseline, possibly because they fail to settle on a single parsing strategy.The authors argue that changing parsing strategies prevents optimization as effectively as the fully sequential LSTM.
  • Latent tree models: Across four 128D runs, LSTM achieves the highest mean accuracy of 71.5, while both latent tree models show relatively high standard deviation.The variability suggests greater susceptibility to poor random initializations.
  • Latent tree models: The failure of both latent tree models on a task where correct parsing ensures success suggests they may be incapable of learning to parse.ListOps strongly encourages correct parsing, linking parsing behavior directly to task performance.

6 Analysis

The analysis finds that RL-SPINN produces the most ground-truth-consistent parses, especially at 128D, but both latent tree models show unstable and often uninterpretable parsing behavior. Expanding training data to as many as 990k examples does not resolve their failure, indicating that data volume is not the critical issue.

  • F1 scores: At 128D, RL-SPINN reaches 71.1 F1 with ground-truth trees and has average tree depth 10.4, close to the ground-truth depth of 9.6.ListOps ground-truth trees have F1 41.6 with left-branching trees versus 9.8 with right-branching trees, matching RL-SPINN’s left-branching tendency.
  • F1 scores: ST-Gumbel’s 128D parses have significantly lower ground-truth F1 than RL-SPINN’s, although they remain better than random trees.RL-SPINN outperforms ST-Gumbel on ListOps by ∼4%.
  • F1 scores: RL-SPINN’s ground-truth F1 ranges from 18.5 to 71.1 across runs, averaging 39.8 with standard deviation 19.4, while ST-Gumbel averages 44.5 with standard deviation 11.8.Both models show poor agreement in parsing decisions across random restarts, with self F1 comparable to randomly generated trees.
  • Parse trees: In examples, RL-SPINN sometimes predicts correctly despite parses differing from the reference, while ST-Gumbel can produce balanced or incorrect compositions.RL-SPINN correctly predicts 7 and 6 in examples, whereas ST-Gumbel predicts 2 and 5; generated parses are not always interpretable when composing several operations.
  • Dataset size: Training expansions to 240k, 540k, and 990k examples show that data volume is not a critical issue preventing these latent tree models from succeeding.ListOps requires a large RNN–TreeRNN gap, but larger datasets do not explain the models’ failure.

7 Conclusion

The paper introduces ListOps as a diagnostic dataset for testing latent tree models’ parsing ability, showing that leading models fail even in a setting designed to encourage parsing. It concludes by calling for shorter-sequence variants and more rigorous latent-tree research.

  • Conclusion: ListOps is a toy diagnostic dataset designed to test latent tree models’ parsing ability through a large RNN–TreeRNN performance gap.It is simpler than natural language while not necessarily being the simplest parsing test.
  • Conclusion: Leading latent tree models are unable to learn to parse ListOps even in a setting that strongly encourages parsing.The experiments tested only two latent tree models; some other leading models could not be trained and analyzed because of high computational complexity.
  • Conclusion: Future work aims to create shorter-sequence ListOps variants that preserve the RNN–TreeRNN gap and develop models that succeed on the task.The authors hope rigorous ListOps testing will inspire latent-tree research and potentially reveal principled grammars useful for sentence representations.
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