Dominican Republic

This paper presents a Parallel Universal Dependency (PUD) treebank for the Indo-Aryan language, Bengali. The treebank consists of 1000 Bengali sentences created using a parallel corpus of English-Bengali and Hindi-Bengali. The number of tokens reported for the 200 manually annotated sentences are 2622. Both the English and the Hindi corpus was taken from the Universal Dependency (UD) repository and subsequently the English corpus was chosen as the source text. The corpus was then translated in Bengali from scratch by the author, who is also a native speaker of the language, and thereafter annotated based on universal parts of speech tag, language specific parts of speech tag and on syntactic levels. The paper also illustrates the linguistic analysis of the PUD treebank and concludes with the kappa score.

EMNLP 2021

Bengali Parallel Universal Dependency Treebank

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EMNLP 2021 is planned to be a hybrid event in Punta Cana, Dominican Republic, with both on-site and fully virtual participation possible.

Feed-forward layers constitute two-thirds of a transformer model's parameters, yet their role in the network remains under-explored. We show that feed-forward layers in transformer-based language models operate as key-value memories, where each key correlates with textual patterns in the training examples, and each value induces a distribution over the output vocabulary. Our experiments show that the learned patterns are human-interpretable, and that lower layers tend to capture shallow patterns, while upper layers learn more semantic ones. The values complement the keys' input patterns by inducing output distributions that concentrate probability mass on tokens likely to appear immediately after each pattern, particularly in the upper layers. Finally, we demonstrate that the output of a feed-forward layer is a composition of its memories, which is subsequently refined throughout the model's layers via residual connections to produce the final output distribution.

Transformer Feed-Forward Layers Are Key-Value Memories

When a model attribution technique highlights a particular part of the input, a user might understand this highlight as making a statement about counterfactuals (Miller, 2019): if that part of the input were to change, the model's prediction might change as well. This paper investigates how well different attribution techniques align with this assumption on realistic counterfactuals in the case of reading comprehension (RC). RC is a particularly challenging test case, as token-level attributions that have been extensively studied in other NLP tasks such as sentiment analysis are less suitable to represent the reasoning that RC models perform. We construct counterfactual sets for three different RC settings, and through heuristics that can connect attribution methods' outputs to high-level model behavior, we can evaluate how useful different attribution methods and even different formats are for understanding counterfactuals. We find that pairwise attributions are better suited to RC than token-level attributions across these different RC settings, with our best performance coming from a modification that we propose to an existing pairwise attribution method.

Connecting Attributions and QA Model Behavior on Realistic Counterfactuals

This paper describes a compact and effective model for low-latency passage retrieval in con- versational search based on learned dense rep- resentations. Prior to our work, the state-of- the-art approach uses a multi-stage pipeline comprising conversational query reformula- tion and information retrieval modules. De- spite its effectiveness, such a pipeline often includes multiple neural models that require long inference times. In addition, indepen- dently optimizing each module ignores de- pendencies among them. To address these shortcomings, we propose to integrate conver- sational query reformulation directly into a dense retrieval model. To aid in this goal, we create a dataset with pseudo-relevance labels for conversational search to overcome the lack of training data and to explore different train- ing strategies. We demonstrate that our model effectively rewrites conversational queries as dense representations in conversational search and open-domain question answering datasets. Finally, after observing that our model learns to adjust the L2 norm of query token embed- dings, we leverage this property for hybrid re- trieval and to support error analysis.

Contextualized Query Embeddings for Conversational Search

The semantic matching capabilities of neural information retrieval can ameliorate synonymy and polysemy problems of symbolic approaches. However, neural models' dense representations are more suitable for re-ranking, due to their inefficiency. Sparse representations, either in symbolic or latent form, are more efficient with an inverted index. Taking the merits of the sparse and dense representations, we propose an ultra-high dimensional (UHD) representation scheme equipped with directly controllable sparsity. UHD's large capacity and minimal noise and interference among the dimensions allow for binarized representations, which are highly efficient for storage and search. Also proposed is a bucketing method, where the embeddings from multiple layers of BERT are selected/merged to represent diverse linguistic aspects. We test our models with MS MARCO and TREC CAR, showing that our model outperforms other sparse models.

Ultra-High Dimensional Sparse Representations with Binarization for Efficient Text Retrieval

With the advent of contextualized embeddings, attention towards neural ranking approaches for Information Retrieval increased considerably. However, two aspects have remained largely neglected: i) queries usually consist of few keywords only, which increases ambiguity and makes their contextualization harder, and ii) performing neural ranking on non-English documents is still cumbersome due to shortage of labeled datasets. In this paper we present SIR (Sense-enhanced Information Retrieval) to mitigate both problems by leveraging word sense information. At the core of our approach lies a novel multilingual query expansion mechanism based on Word Sense Disambiguation that provides sense definitions as additional semantic information for the query. Importantly, we use senses as a bridge across languages, thus allowing our model to perform considerably better than its supervised and unsupervised alternatives across French, German, Italian and Spanish languages on several CLEF benchmarks, while being trained on English Robust04 data only.

IR like a SIR: Sense-enhanced Information Retrieval for Multiple Languages

Neural topic models (NTMs) apply deep neural networks to topic modelling. Despite their success, NTMs generally ignore two important aspects: (1) only document-level word count information is utilized for the training, while more fine-grained sentence-level information is ignored, and (2) external semantic knowledge regarding documents, sentences and words are not exploited for the training. To address these issues, we propose a variational autoencoder (VAE) NTM model that jointly reconstructs the sentence and document word counts using combinations of bag-of-words (BoW) topical embeddings and pre-trained semantic embeddings. The pre-trained embeddings are first transformed into a common latent topical space to align their semantics with the BoW embeddings. Our model also features hierarchical KL divergence to leverage embeddings of each document to regularize those of their sentences, thereby paying more attention to semantically relevant sentences. Both quantitative and qualitative experiments have shown the efficacy of our model in 1) lowering the reconstruction errors at both the sentence and document levels, and 2) discovering more coherent topics from real-world datasets.

Neural Attention-Aware Hierarchical Topic Model

As a prominent attribution-based explanation algorithm, Integrated Gradients (IG) is widely adopted due to its desirable explanation axioms and the ease of gradient computation. It measures feature importance by averaging the model’s output gradient interpolated along a straight-line path in the input data space. However, such straight-line interpolated points are not representative of text data due to the inherent discreteness of the word embedding space. This questions the faithfulness of the gradients computed at the interpolated points and consequently, the quality of the generated explanations. Here we propose Discretized Integrated Gradients (DIG), which allows effective attribution along non-linear interpolation paths. We develop two interpolation strategies for the discrete word embedding space that generates interpolation points that lie close to actual words in the embedding space, yielding more faithful gradient computation. We demonstrate the effectiveness of DIG over IG through experimental and human evaluations on multiple sentiment classification datasets. We provide the source code of DIG to encourage reproducible research.

Discretized Integrated Gradients for Explaining Language Models

The exponential growth in the scientific literature in recent years has to lead to a serious scholarly information overload on researchers. Automatically identifying the specific contribution made in a research paper would help quicker comprehension of the work, faster literature survey, comparison with the related works, etc. Here in this work, we propose a method to automatically extract the contribution statements from research articles. We design a model (ContriSci) multi-task deep neural network leveraging section identification and citance classification of scientific statements to predict whether a given scientific sentence specifies a contribution or not. Our long-term goal, we create a knowledge graph of scientific contributions for machine comprehension and more straightforward navigation of research contributions for a particular domain. Our proposed model achieves the best performance over earlier methods (a relative improvement of 8.08% in terms ofF1score) for contributing sentence identification over a dataset of NaturalLanguage Processing (NLP) papers.

SciBERT-based Multitasking Deep Neural Architecture to identify Contribution Statements from Scientific articles

The goal of this project was to characterize the discussions surrounding academic test anxiety on the social media platform, Reddit.

Characterizing Test Anxiety on Social Media

A challenge for targeted sentiment analysis is that most datasets are domain-specific and thus building supervised models for a new target domain requires substantial annotation effort. Domain adaptation for this task has two dimensions: the nature of targets and the opinion words used to describe sentiment towards the target. We present a data sampling strategy informed by domain differences across these two dimensions with the goal of selecting a small number of examples, thereby minimizing annotation effort. This obtains performance in the 86-100% range compared to the full supervised model using only 4-15% of the full training data.

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