ymoslem/acl-abstracts huggingface dataset:
Handling Anomalies of Synthetic Questions in Unsupervised Question Answering (2020)

Advances in Question Answering (QA) research require additional datasets for new domains, languages, and types of questions, as well as for performance increases. Human creation of a QA dataset like SQuAD, however, is expensive. As an alternative, an unsupervised QA approach has been proposed so that QA training data can be generated automatically. However, the performance of unsupervised QA is much lower than that of supervised QA models. We identify two anomalies in the automatically generated questions and propose how they can be mitigated. We show our approach helps improve unsupervised QA significantly across a number of QA tasks.
Designing Templates for Eliciting Commonsense Knowledge from Pretrained Sequence-to-Sequence Models (2020)

While internalized {\textquotedblleft}implicit knowledge{\textquotedblright} in pretrained transformers has led to fruitful progress in many natural language understanding tasks, how to most effectively elicit such knowledge remains an open question. Based on the text-to-text transfer transformer (T5) model, this work explores a template-based approach to extract implicit knowledge for commonsense reasoning on multiple-choice (MC) question answering tasks. Experiments on three representative MC datasets show the surprisingly good performance of our simple template, coupled with a logit normalization technique for disambiguation. Furthermore, we verify that our proposed template can be easily extended to other MC tasks with contexts such as supporting facts in open-book question answering settings. Starting from the MC task, this work initiates further research to find generic natural language templates that can effectively leverage stored knowledge in pretrained models.
Towards the First Machine Translation System for Sumerian Transliterations (2020)

The Sumerian cuneiform script was invented more than 5,000 years ago and represents one of the oldest in history. We present the first attempt to translate Sumerian texts into English automatically. We publicly release high-quality corpora for standardized training and evaluation and report results on experiments with supervised, phrase-based, and transfer learning techniques for machine translation. Quantitative and qualitative evaluations indicate the usefulness of the translations. Our proposed methodology provides a broader audience of researchers with novel access to the data, accelerates the costly and time-consuming manual translation process, and helps them better explore the relationships between Sumerian cuneiform and Mesopotamian culture.
Using Bilingual Patents for Translation Training (2020)

While bilingual corpora have been instrumental for machine translation, their utility for training translators has been less explored. We investigate the use of bilingual corpora as pedagogical tools for translation in the technical domain. In a user study, novice translators revised Chinese translations of English patents through bilingual concordancing. Results show that concordancing with an in-domain bilingual corpus can yield greater improvement in translation quality of technical terms than a general-domain bilingual corpus.
Federated Learning for Spoken Language Understanding (2020)

Recently, spoken language understanding (SLU) has attracted extensive research interests, and various SLU datasets have been proposed to promote the development. However, most of the existing methods focus on a single individual dataset, the efforts to improve the robustness of models and obtain better performance by combining the merits of various datasets are not well studied. In this paper, we argue that if these SLU datasets are considered together, different knowledge from different datasets could be learned jointly, and there are high chances to promote the performance of each dataset. At the same time, we further attempt to prevent data leakage when unifying multiple datasets which, arguably, is more useful in an industry setting. To this end, we propose a federated learning framework, which could unify various types of datasets as well as tasks to learn and fuse various types of knowledge, i.e., text representations, from different datasets and tasks, without the sharing of downstream task data. The fused text representations merge useful features from different SLU datasets and tasks and are thus much more powerful than the original text representations alone in individual tasks. At last, in order to provide multi-granularity text representations for our framework, we propose a novel Multi-view Encoder (MV-Encoder) as the backbone of our federated learning framework. Experiments on two SLU benchmark datasets, including two tasks (intention detection and slot filling) and federated learning settings (horizontal federated learning, vertical federated learning and federated transfer learning), demonstrate the effectiveness and universality of our approach. Specifically, we are able to get 1.53{\%} improvement on the intent detection metric accuracy. And we could also boost the performance of a strong baseline by up to 5.29{\%} on the slot filling metric F1. Furthermore, by leveraging BERT as an additional encoder, we establish new state-of-the-art results on SNIPS and ATIS datasets, where we get 99.33{\%} and 98.28{\%} in terms of accuracy on intent detection task as well as 97.20{\%} and 96.41{\%} in terms of F1 score on slot filling task, respectively.
Attentively Embracing Noise for Robust Latent Representation in BERT (2020)

Modern digital personal assistants interact with users through voice. Therefore, they heavily rely on automatic speech recognition (ASR) in order to convert speech to text and perform further tasks. We introduce EBERT, which stands for EmbraceBERT, with the goal of extracting more robust latent representations for the task of noisy ASR text classification. Conventionally, BERT is fine-tuned for downstream classification tasks using only the [CLS] starter token, with the remaining tokens being discarded. We propose using all encoded transformer tokens and further encode them using a novel attentive embracement layer and multi-head attention layer. This approach uses the otherwise discarded tokens as a source of additional information and the multi-head attention in conjunction with the attentive embracement layer to select important features from clean data during training. This allows for the extraction of a robust latent vector resulting in improved classification performance during testing when presented with noisy inputs. We show the impact of our model on both the Chatbot and Snips corpora for intent classification with ASR error. Results, in terms of F1-score and mean between 10 runs, show that our model significantly outperforms the baseline model.
A Comprehensive Evaluation of Incremental Speech Recognition and Diarization for Conversational AI (2020)

Automatic Speech Recognition (ASR) systems are increasingly powerful and more accurate, but also more numerous with several options existing currently as a service (e.g. Google, IBM, and Microsoft). Currently the most stringent standards for such systems are set within the context of their use in, and for, Conversational AI technology. These systems are expected to operate incrementally in real-time, be responsive, stable, and robust to the pervasive yet peculiar characteristics of conversational speech such as disfluencies and overlaps. In this paper we evaluate the most popular of such systems with metrics and experiments designed with these standards in mind. We also evaluate the speaker diarization (SD) capabilities of the same systems which will be particularly important for dialogue systems designed to handle multi-party interaction. We found that Microsoft has the leading incremental ASR system which preserves disfluent materials and IBM has the leading incremental SD system in addition to the ASR that is most robust to speech overlaps. Google strikes a balance between the two but none of these systems are yet suitable to reliably handle natural spontaneous conversations in real-time.
Decolonising Speech and Language Technology (2020)

After generations of exploitation, Indigenous people often respond negatively to the idea that their languages are data ready for the taking. By treating Indigenous knowledge as a commodity, speech and language technologists risk disenfranchising local knowledge authorities, reenacting the causes of language endangerment. Scholars in related fields have responded to calls for decolonisation, and we in the speech and language technology community need to follow suit, and explore what this means for our practices that involve Indigenous languages and the communities who own them. This paper reviews colonising discourses in speech and language technology, and suggests new ways of working with Indigenous communities, and seeks to open a discussion of a postcolonial approach to computational methods for supporting language vitality.
Dual-decoder Transformer for Joint Automatic Speech Recognition and Multilingual Speech Translation (2020)

We introduce dual-decoder Transformer, a new model architecture that jointly performs automatic speech recognition (ASR) and multilingual speech translation (ST). Our models are based on the original Transformer architecture (Vaswani et al., 2017) but consist of two decoders, each responsible for one task (ASR or ST). Our major contribution lies in how these decoders interact with each other: one decoder can attend to different information sources from the other via a dual-attention mechanism. We propose two variants of these architectures corresponding to two different levels of dependencies between the decoders, called the parallel and cross dual-decoder Transformers, respectively. Extensive experiments on the MuST-C dataset show that our models outperform the previously-reported highest translation performance in the multilingual settings, and outperform as well bilingual one-to-one results. Furthermore, our parallel models demonstrate no trade-off between ASR and ST compared to the vanilla multi-task architecture. Our code and pre-trained models are available at \url{https://github.com/formiel/speech-translation}.
Multitask Learning-Based Neural Bridging Reference Resolution (2020)

We propose a multi task learning-based neural model for resolving bridging references tackling two key challenges. The first challenge is the lack of large corpora annotated with bridging references. To address this, we use multi-task learning to help bridging reference resolution with coreference resolution. We show that substantial improvements of up to 8 p.p. can be achieved on full bridging resolution with this architecture. The second challenge is the different definitions of bridging used in different corpora, meaning that hand-coded systems or systems using special features designed for one corpus do not work well with other corpora. Our neural model only uses a small number of corpus independent features, thus can be applied to different corpora. Evaluations with very different bridging corpora (ARRAU, ISNOTES, BASHI and SCICORP) suggest that our architecture works equally well on all corpora, and achieves the SoTA results on full bridging resolution for all corpora, outperforming the best reported results by up to 36.3 p.p..
Improving Human-Labeled Data through Dynamic Automatic Conflict Resolution (2020)

This paper develops and implements a scalable methodology for (a) estimating the noisiness of labels produced by a typical crowdsourcing semantic annotation task, and (b) reducing the resulting error of the labeling process by as much as 20-30{\%} in comparison to other common labeling strategies. Importantly, this new approach to the labeling process, which we name Dynamic Automatic Conflict Resolution (DACR), does not require a ground truth dataset and is instead based on inter-project annotation inconsistencies. This makes DACR not only more accurate but also available to a broad range of labeling tasks. In what follows we present results from a text classification task performed at scale for a commercial personal assistant, and evaluate the inherent ambiguity uncovered by this annotation strategy as compared to other common labeling strategies.
Automatic Discovery of Heterogeneous Machine Learning Pipelines: An Application to Natural Language Processing (2020)

This paper presents AutoGOAL, a system for automatic machine learning (AutoML) that uses heterogeneous techniques. In contrast with existing AutoML approaches, our contribution can automatically build machine learning pipelines that combine techniques and algorithms from different frameworks, including shallow classifiers, natural language processing tools, and neural networks. We define the heterogeneous AutoML optimization problem as the search for the best sequence of algorithms that transforms specific input data into the desired output. This provides a novel theoretical and practical approach to AutoML. Our proposal is experimentally evaluated in diverse machine learning problems and compared with alternative approaches, showing that it is competitive with other AutoML alternatives in standard benchmarks. Furthermore, it can be applied to novel scenarios, such as several NLP tasks, where existing alternatives cannot be directly deployed. The system is freely available and includes in-built compatibility with a large number of popular machine learning frameworks, which makes our approach useful for solving practical problems with relative ease and effort.
Distill and Replay for Continual Language Learning (2020)

Accumulating knowledge to tackle new tasks without necessarily forgetting the old ones is a hallmark of human-like intelligence. But the current dominant paradigm of machine learning is still to train a model that works well on static datasets. When learning tasks in a stream where data distribution may fluctuate, fitting on new tasks often leads to forgetting on the previous ones. We propose a simple yet effective framework that continually learns natural language understanding tasks with one model. Our framework distills knowledge and replays experience from previous tasks when fitting on a new task, thus named DnR (distill and replay). The framework is based on language models and can be smoothly built with different language model architectures. Experimental results demonstrate that DnR outperfoms previous state-of-the-art models in continually learning tasks of the same type but from different domains, as well as tasks of different types. With the distillation method, we further show that it`s possible for DnR to incrementally compress the model size while still outperforming most of the baselines. We hope that DnR could promote the empirical application of continual language learning, and contribute to building human-level language intelligence minimally bothered by catastrophic forgetting.
Incorporating Noisy Length Constraints into Transformer with Length-aware Positional Encodings (2020)

Neural Machine Translation often suffers from an under-translation problem due to its limited modeling of output sequence lengths. In this work, we propose a novel approach to training a Transformer model using length constraints based on length-aware positional encoding (PE). Since length constraints with exact target sentence lengths degrade translation performance, we add random noise within a certain window size to the length constraints in the PE during the training. In the inference step, we predict the output lengths using input sequences and a BERT-based length prediction model. Experimental results in an ASPEC English-to-Japanese translation showed the proposed method produced translations with lengths close to the reference ones and outperformed a vanilla Transformer (especially in short sentences) by 3.22 points in BLEU. The average translation results using our length prediction model were also better than another baseline method using input lengths for the length constraints. The proposed noise injection improved robustness for length prediction errors, especially within the window size.
Rethinking Skip Connection with Layer Normalization (2020)

Skip connection is a widely-used technique to improve the performance and the convergence of deep neural networks, which is believed to relieve the difficulty in optimization due to non-linearity by propagating a linear component through the neural network layers. However, from another point of view, it can also be seen as a modulating mechanism between the input and the output, with the input scaled by a pre-defined value one. In this work, we investigate how the scale factors in the effectiveness of the skip connection and reveal that a trivial adjustment of the scale will lead to spurious gradient exploding or vanishing in line with the deepness of the models, which could by addressed by normalization, in particular, layer normalization, which induces consistent improvements over the plain skip connection. Inspired by the findings, we further propose to adaptively adjust the scale of the input by recursively applying skip connection with layer normalization, which promotes the performance substantially and generalizes well across diverse tasks including both machine translation and image classification datasets.
Specializing Word Vectors by Spectral Decomposition on Heterogeneously Twisted Graphs (2020)

Traditional word vectors, such as word2vec and glove, have a well-known inclination to conflate the semantic similarity with other semantic relations. A retrofitting procedure may be needed to solve this issue. In this work, we propose a new retrofitting method called Heterogeneously Retrofitted Spectral Word Embedding. It heterogeneously twists the similarity matrix of word pairs with lexical constraints. A new set of word vectors is generated by a spectral decomposition of the similarity matrix, which has a linear algebraic analytic form. Our method has a competitive performance compared with the state-of-the-art retrofitting method such as AR (CITATION). In addition, since our embedding has a clear linear algebraic relationship with the similarity matrix, we carefully study the contribution of each component in our model. Last but not least, our method is very efficient to execute.
Deep Inside-outside Recursive Autoencoder with All-span Objective (2020)

Deep inside-outside recursive autoencoder (DIORA) is a neural-based model designed for unsupervised constituency parsing. During its forward computation, it provides phrase and contextual representations for all spans in the input sentence. By utilizing the contextual representation of each leaf-level span, the span of length 1, to reconstruct the word inside the span, the model is trained without labeled data. In this work, we extend the training objective of DIORA by making use of all spans instead of only leaf-level spans. We test our new training objective on datasets of two languages: English and Japanese, and empirically show that our method achieves improvement in parsing accuracy over the original DIORA.
Cross-Lingual Document Retrieval with Smooth Learning (2020)

Cross-lingual document search is an information retrieval task in which the queries' language and the documents' language are different. In this paper, we study the instability of neural document search models and propose a novel end-to-end robust framework that achieves improved performance in cross-lingual search with different documents' languages. This framework includes a novel measure of the relevance, smooth cosine similarity, between queries and documents, and a novel loss function, Smooth Ordinal Search Loss, as the objective function. We further provide theoretical guarantee on the generalization error bound for the proposed framework. We conduct experiments to compare our approach with other document search models, and observe significant gains under commonly used ranking metrics on the cross-lingual document retrieval task in a variety of languages.
Increasing Learning Efficiency of Self-Attention Networks through Direct Position Interactions, Learnable Temperature, and Convoluted Attention (2020)

Self-Attention Networks (SANs) are an integral part of successful neural architectures such as Transformer (Vaswani et al., 2017), and thus of pretrained language models such as BERT (Devlin et al., 2019) or GPT-3 (Brown et al., 2020). Training SANs on a task or pretraining them on language modeling requires large amounts of data and compute resources. We are searching for modifications to SANs that enable faster learning, i.e., higher accuracies after fewer update steps. We investigate three modifications to SANs: direct position interactions, learnable temperature, and convoluted attention. When evaluating them on part-of-speech tagging, we find that direct position interactions are an alternative to position embeddings, and convoluted attention has the potential to speed up the learning process.
Picking BERT`s Brain: Probing for Linguistic Dependencies in Contextualized Embeddings Using Representational Similarity Analysis (2020)

As the name implies, contextualized representations of language are typically motivated by their ability to encode context. Which aspects of context are captured by such representations? We introduce an approach to address this question using Representational Similarity Analysis (RSA). As case studies, we investigate the degree to which a verb embedding encodes the verb`s subject, a pronoun embedding encodes the pronoun`s antecedent, and a full-sentence representation encodes the sentence`s head word (as determined by a dependency parse). In all cases, we show that BERT`s contextualized embeddings reflect the linguistic dependency being studied, and that BERT encodes these dependencies to a greater degree than it encodes less linguistically-salient controls. These results demonstrate the ability of our approach to adjudicate between hypotheses about which aspects of context are encoded in representations of language.
The Devil is in the Details: Evaluating Limitations of Transformer-based Methods for Granular Tasks (2020)

Contextual embeddings derived from transformer-based neural language models have shown state-of-the-art performance for various tasks such as question answering, sentiment analysis, and textual similarity in recent years. Extensive work shows how accurately such models can represent abstract, semantic information present in text. In this expository work, we explore a tangent direction and analyze such models' performance on tasks that require a more granular level of representation. We focus on the problem of textual similarity from two perspectives: matching documents on a granular level (requiring embeddings to capture fine-grained attributes in the text), and an abstract level (requiring embeddings to capture overall textual semantics). We empirically demonstrate, across two datasets from different domains, that despite high performance in abstract document matching as expected, contextual embeddings are consistently (and at times, vastly) outperformed by simple baselines like TF-IDF for more granular tasks. We then propose a simple but effective method to incorporate TF-IDF into models that use contextual embeddings, achieving relative improvements of up to 36{\%} on granular tasks.