Combining Modular Skills in Multitask Learning

A modular design encourages neural models to disentangle and recombine different facets of knowledge to generalise more systematically to new tasks. In this work, we assume that each task is associated with a subset of latent discrete skills from a (potentially small) inventory. In turn, skills correspond to parameter-efficient (sparse / low-rank) model parameterisations. By jointly learning these and a task-skill allocation matrix, the network for each task is instantiated as the average of the parameters of active skills. To favour non-trivial soft partitions of skills across tasks, we experiment with a series of inductive biases, such as an Indian Buffet Process prior and a two-speed learning rate. We evaluate our latent-skill model on two main settings: 1) multitask reinforcement learning for grounded instruction following on 8 levels of the BabyAI platform; and 2) few-shot adaptation of pre-trained text-to-text generative models on CrossFit, a benchmark comprising 160 NLP tasks. We find that the modular design of a network significantly increases sample efficiency in reinforcement learning and few-shot generalisation in supervised learning, compared to baselines with fully shared, task-specific, or conditionally generated parameters where knowledge is entangled across tasks. In addition, we show how discrete skills help interpretability, as they yield an explicit hierarchy of tasks.

IGLUE: A Benchmark for Transfer Learning across Modalities, Tasks, and Languages

Reliable evaluation benchmarks designed for replicability and comprehensiveness have driven progress in machine learning. Due to the lack of a multilingual benchmark, however, vision-and-language research has mostly focused on English language tasks. To fill this gap, we introduce the Image-Grounded Language Understanding Evaluation benchmark. IGLUE brings together - by both aggregating pre-existing datasets and creating new ones - visual question answering, cross-modal retrieval, grounded reasoning, and grounded entailment tasks across 20 diverse languages. Our benchmark enables the evaluation of multilingual multimodal models for transfer learning, not only in a zero-shot setting, but also in newly defined few-shot learning setups. Based on the evaluation of the available state-of-the-art models, we find that translate-test transfer is superior to zero-shot transfer and that few-shot learning is hard to harness for many tasks. Moreover, downstream performance is partially explained by the amount of available unlabelled textual data for pretraining, and only weakly by the typological distance of target-source languages. We hope to encourage future research efforts in this area by releasing the benchmark to the community.

Does entity abstraction help generative Transformers reason?

Pre-trained language models (LMs) often struggle to reason logically or generalize in a compositional fashion. Recent work suggests that incorporating external entity knowledge can improve LMs' abilities to reason and generalize. However, the effect of explicitly providing entity abstraction remains unclear, especially with recent studies suggesting that pre-trained LMs already encode some of that knowledge in their parameters. We study the utility of incorporating entity type abstractions into pre-trained Transformers and test these methods on four NLP tasks requiring different forms of logical reasoning: (1) compositional language understanding with text-based relational reasoning (CLUTRR), (2) abductive reasoning (ProofWriter), (3) multi-hop question answering (HotpotQA), and (4) conversational question answering (CoQA). We propose and empirically explore three ways to add such abstraction: (i) as additional input embeddings, (ii) as a separate sequence to encode, and (iii) as an auxiliary prediction task for the model. Overall, our analysis demonstrates that models with abstract entity knowledge performs better than without it. However, our experiments also show that the benefits strongly depend on the technique used and the task at hand. The best abstraction aware models achieved an overall accuracy of 88.8% and 91.8% compared to the baseline model achieving 62.3% and 89.8% on CLUTRR and ProofWriter respectively. In addition, abstraction-aware models showed improved compositional generalization in both interpolation and extrapolation settings. However, for HotpotQA and CoQA, we find that F1 scores improve by only 0.5% on average. Our results suggest that the benefit of explicit abstraction is significant in formally defined logical reasoning settings requiring many reasoning hops, but point to the notion that it is less beneficial for NLP tasks having less formal logical structure.

An Empirical Survey of the Effectiveness of Debiasing Techniques for Pre-Trained Language Models

Recent work has shown that pre-trained language models capture social biases from the text corpora they are trained on. This has attracted attention to developing techniques that mitigate such biases. In this work, we perform a empirical survey of five recently proposed debiasing techniques: Counterfactual Data Augmentation (CDA), Dropout, Iterative Nullspace Projection, Self-Debias, and SentenceDebias. We quantify the effectiveness of each technique using three different bias benchmarks while also measuring the impact of these techniques on a model's language modeling ability, as well as its performance on downstream NLU tasks. We experimentally find that: (1) CDA and Self-Debias are the strongest of the debiasing techniques, obtaining improved scores on most of the bias benchmarks (2) Current debiasing techniques do not generalize well beyond gender bias; And (3) improvements on bias benchmarks such as StereoSet and CrowS-Pairs by using debiasing strategies are usually accompanied by a decrease in language modeling ability, making it difficult to determine whether the bias mitigation is effective.

The Power of Prompt Tuning for Low-Resource Semantic Parsing

Prompt tuning has recently emerged as an effective method for adapting pre-trained language models to a number of language tasks. In this paper, we investigate prompt tuning for semantic parsing, the task of mapping natural language utterances onto formal meaning representations. For large T5 models we find (i) that prompt tuning significantly outperforms fine-tuning in the low data regime and (ii) that canonicalization -- i.e. naturalizing the meaning representations -- barely improves performance. This last result is surprising as it suggests that large T5 models can be modulated to generate sequences that are far from the pre-training distribution.

Evaluating the Faithfulness of Importance Measures in NLP by Recursively Masking Allegedly Important Tokens and Retraining

To explain NLP models, many methods inform which inputs tokens are important for a prediction. However, an open question is if these methods accurately reflect the model's logic, a property often called faithfulness. In this work, we adapt and improve a recently proposed faithfulness benchmark from computer vision called ROAR (RemOve And Retrain), by Hooker et al. (2019). We improve ROAR by recursively removing dataset redundancies, which otherwise interfere with ROAR. We adapt and apply ROAR, to popular NLP importance measures, namely attention, gradient, and integrated gradients. Additionally, we use mutual information as an additional baseline. Evaluation is done on a suite of classification tasks often used in the faithfulness of attention literature. Finally, we propose a scalar faithfulness metric, which makes it easy to compare results across papers. We find that, importance measures considered to be unfaithful for computer vision tasks perform favorably for NLP tasks, the faithfulness of an importance measure is task-dependent, and the computational overhead of integrated gradient is rarely justified.

Compositional Generalization in Dependency Parsing

Compositionality, or the ability to combine familiar units like words into novel phrases and sentences, has been the focus of intense interest in artificial intelligence in recent years. To test compositional generalization in semantic parsing, Keysers et al. (2020) introduced Compositional Freebase Queries (CFQ). This dataset maximizes the similarity between the test and train distributions over primitive units, like words, while maximizing the compound divergence: the dissimilarity between test and train distributions over larger structures, like phrases. Dependency parsing, however, lacks a compositional generalization benchmark. In this work, we introduce a gold-standard set of dependency parses for CFQ, and use this to analyze the behavior of a state-of-the art dependency parser (Qi et al., 2020) on the CFQ dataset. We find that increasing compound divergence degrades dependency parsing performance, although not as dramatically as semantic parsing performance. Additionally, we find the performance of the dependency parser does not uniformly degrade relative to compound divergence, and the parser performs differently on different splits with the same compound divergence. We explore a number of hypotheses for what causes the non-uniform degradation in dependency parsing performance, and identify a number of syntactic structures that drive the dependency parser's lower performance on the most challenging splits.

TopiOCQA: Open-domain Conversational Question Answeringwith Topic Switching

In a conversational question answering scenario, a questioner seeks to extract information about a topic through a series of interdependent questions and answers. As the conversation progresses, they may switch to related topics, a phenomenon commonly observed in information-seeking search sessions. However, current datasets for conversational question answering are limiting in two ways: 1) they do not contain topic switches; and 2) they assume the reference text for the conversation is given, i.e., the setting is not open-domain. We introduce TopiOCQA (pronounced Tapioca), an open-domain conversational dataset with topic switches on Wikipedia. TopiOCQA contains 3,920 conversations with information-seeking questions and free-form answers. TopiOCQA poses a challenging test-bed for models, where efficient retrieval is required on multiple turns of the same conversation, in conjunction with constructing valid responses using conversational history. We evaluate several baselines, by combining state-of-the-art document retrieval methods with neural reader models. Our best models achieves F1 of 51.9, and BLEU score of 42.1 which falls short of human performance by 18.3 points and 17.6 points respectively, indicating the difficulty of our dataset. Our dataset and code will be available at https://mcgill-nlp.github.io/topiocqa

Visually Grounded Reasoning across Languages and Cultures

The design of widespread vision-and-language datasets and pre-trained encoders directly adopts, or draws inspiration from, the concepts and images of ImageNet. While one can hardly overestimate how much this benchmark contributed to progress in computer vision, it is mostly derived from lexical databases and image queries in English, resulting in source material with a North American or Western European bias. Therefore, we devise a new protocol to construct an ImageNet-style hierarchy representative of more languages and cultures. In particular, we let the selection of both concepts and images be entirely driven by native speakers, rather than scraping them automatically. Specifically, we focus on a typologically diverse set of languages, namely, Indonesian, Mandarin Chinese, Swahili, Tamil, and Turkish. On top of the concepts and images obtained through this new protocol, we create a multilingual dataset for {M}ulticultur{a}l {R}easoning over {V}ision and {L}anguage (MaRVL) by eliciting statements from native speaker annotators about pairs of images. The task consists of discriminating whether each grounded statement is true or false. We establish a series of baselines using state-of-the-art models and find that their cross-lingual transfer performance lags dramatically behind supervised performance in English. These results invite us to reassess the robustness and accuracy of current state-of-the-art models beyond a narrow domain, but also open up new exciting challenges for the development of truly multilingual and multicultural systems.

Post-hoc Interpretability for Neural NLP: A Survey

Natural Language Processing (NLP) models have become increasingly more complex and widespread. With recent developments in neural networks, a growing concern is whether it is responsible to use these models. Concerns such as safety and ethics can be partially addressed by providing explanations. Furthermore, when models do fail, providing explanations is paramount for accountability purposes. To this end, interpretability serves to provide these explanations in terms that are understandable to humans. Central to what is understandable is how explanations are communicated. Therefore, this survey provides a categorization of how recent interpretability methods communicate explanations and discusses the methods in depth. Furthermore, the survey focuses on post-hoc methods, which provide explanations after a model is learned and generally model-agnostic. A common concern for this class of methods is whether they accurately reflect the model. Hence, how these post-hoc methods are evaluated is discussed throughout the paper.