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Existing supervised models for text clustering find it difficult to directly optimize for clustering results. This is because clustering is a discrete process and it is difficult to estimate meaningful gradient of any discrete function that can drive gradient based optimization algorithms. So, existing supervised clustering algorithms indirectly optimize for some continuous function that approximates the clustering process. We propose a scalable training strategy that directly optimizes for a discrete clustering metric. We train a BERT-based embedding model using our method and evaluate it on two publicly available datasets. We show that our method outperforms another BERT-based embedding model employing Triplet loss and other unsupervised baselines. This suggests that optimizing directly for the clustering outcome indeed yields better representations suitable for clustering.

IJCNLP-AACL 2021

Learn The Big Picture: Representation Learning for Clustering

passage embedding

representation learning

supervised clustering

deep learning

bert

**Welcome to ACL-IJCNLP 2021!**

The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP). 

As in previous years, the program of the conference includes a poster session, tutorials, workshops and demonstrations in addition to the main conference.


We were able to keep the registration fees similar to those charged for the virtual ACL 2020. The one fee allows attendance at the main conference and any/all tutorials and workshops. These fees would be $125 Regular Early and $175 Regular Late; $50 Student Early and $75 Student Late. Early registration closes at midnight July 11, 2021 (Eastern Daylight Time).

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The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP).

workshop paper

Crowdworker-constructed natural language inference (NLI) datasets have been found to contain statistical artifacts associated with the annotation process that allow hypothesis-only classifiers to achieve better-than-random performance~\citep{poliak-etal-2018-hypothesis, gururangan2018annotation,tsuchiya-2018-performance}. We investigate whether MedNLI, a physician-annotated dataset with premises extracted from clinical notes, contains such artifacts~\citep{romanov2018lessons}. 

We find that entailed hypotheses contain generic versions of specific concepts in the premise, as well as modifiers related to responsiveness, duration, and probability. Neutral hypotheses feature conditions and behaviors that co-occur with, or cause, the condition(s) in the premise. Contradiction hypotheses feature explicit negation of the premise and implicit negation via assertion of good health. Adversarial filtering demonstrates that performance degrades when evaluated on the \emph{difficult} subset. We provide partition information and recommendations for alternative dataset construction strategies for knowledge-intensive domains.

MedNLI Is Not Immune: Natural Language Inference Artifacts in the Clinical Domain

In adversarial data collection (ADC), a human workforce interacts with a model in real time, attempting to produce examples that elicit incorrect predictions. Researchers hope that models trained on these more challenging datasets will rely less on superficial patterns, and thus be less brittle. However, despite ADC's intuitive appeal, it remains unclear when training on adversarial datasets produces more robust models. In this paper, we conduct a large-scale controlled study focused on question answering, assigning workers at random to compose questions either (i) adversarially (with a model in the loop); or (ii) in the standard fashion (without a model). Across a variety of models and datasets, we find that models trained on adversarial data usually perform better 
on other adversarial datasets but worse on a diverse collection of out-of-domain evaluation sets. Finally, we provide a qualitative analysis of adversarial (vs standard) data, identifying key differences and offering guidance for future research.

On the Efficacy of Adversarial Data Collection for Question Answering: Results from a Large-Scale Randomized Study

Open-domain question answering can be reformulated as a phrase retrieval problem, without the need for processing documents on-demand during inference (Seo et al., 2019).
However, current phrase retrieval models heavily depend on sparse representations and still underperform retriever-reader approaches.
In this work, we show for the first time that we can learn dense representations of phrases alone that achieve much stronger performance in open-domain QA.
We present an effective method to learn phrase representations from the supervision of reading comprehension tasks, coupled with novel negative sampling methods.
We also propose a query-side fine-tuning strategy, which can support transfer learning and reduce the discrepancy between training and inference.
On five popular open-domain QA datasets, our model DensePhrases improves over previous phrase retrieval models by 15%-25% absolute accuracy and matches the performance of state-of-the-art retriever-reader models.
Our model is easy to parallelize due to pure dense representations and processes more than 10 questions per second on CPUs.
Finally, we directly use our pre-indexed dense phrase representations for two slot filling tasks, showing the promise of utilizing DensePhrases as a dense knowledge base for downstream tasks.

Learning Dense Representations of Phrases at Scale

Recent work on training neural retrievers for open-domain question answering (OpenQA) has employed both supervised and unsupervised approaches. However, it remains unclear how unsupervised and supervised methods can be used most effectively for neural retrievers. In this work, we systematically study retriever pre-training. We first propose an approach of unsupervised pre-training with the Inverse Cloze Task and masked salient spans, followed by supervised finetuning using question-context pairs. This approach leads to absolute gains of 2+ points over the previous best result in the top-20 retrieval accuracy on Natural Questions and TriviaQA datasets. We next explore two approaches for end-to-end training of the reader and retriever components in OpenQA models, which differ in the manner the reader ingests the retrieved documents. 
Our experiments demonstrate the effectiveness of these approaches as we obtain state-of-the-art results. On the Natural Questions dataset, we obtain a top-20 retrieval accuracy of 84%, an improvement of 5 points over the recent DPR model. We also achieve good results on answer extraction, outperforming recent models like REALM and RAG by 3+ points.

End-to-End Training of Neural Retrievers for Open-Domain Question Answering

The ability to learn from limited data, or few-shot learning, is a desirable and often critical requirement for NLP systems. While many existing methods do poorly at learning from a handful of examples, large pretrained language models have recently been shown to be efficient few-shot learners. One approach to few-shot learning, which does not require finetuning of model parameters, is to augment the language model's input with priming text which is typically constructed using task specific descriptions and examples. In this work, we further explore priming-based few-shot learning, with focus on using examples as prompts. We show that presenting examples in the right order is key for generalization. We introduce PERO (Prompting with Examples in the Right Order), where we formulate few-shot learning as search over the set of permutations of the training examples. We show that PERO can learn to generalize efficiently using as few as 10 examples, in contrast to existing approaches. While the newline token is a natural choice for separating the examples in the prompt, we show that learning a new separator token can potentially provide further gains in performance. We demonstrate the effectiveness of the proposed method on the tasks of sentiment classification, natural language inference and fact retrieval. Finally, we analyze the learned prompts to reveal novel insights, including the idea that two training examples in the right order alone can provide competitive performance for sentiment classification and natural language inference.

Reordering Examples Helps during Priming-based Few-Shot Learning

Named Entity Recognition (NER) in low-resource languages has been a long-standing challenge in NLP. Recent work has shown great progress in two directions: developing cross-lingual features/models to transfer knowledge to low-resource languages, and translating source-language training data into low-resource target-language training data by projecting annotations with cheap resources. We focus on the second direction in this study. Existing methods suffer from the low quality of the resulting annotated data in the target language; for example, they cannot handle word order and lexical ambiguity well. To handle these limitations we propose a novel approach that uses the projected annotation to generate pseudo supervised data with a transformer language model and a constrained beam search. This allows us to generate more diverse, higher quality, as well as higher quantities of annotated data in the target language. Experiments demonstrate that, when combining our method with available cross-lingual features, it achieves state-of-the-art or competitive performance on NER in a low-resource setting, especially for languages that are distant from our source language, English.

Constrained Labeled Data Generation for Low-Resource Named Entity Recognition

We explore task-adaptive pretraining (TAPT) with adapters and show that directly fine-tuning adapters can achieve comparable performance to TAPT while reducing training time by skipping the second phase of pretraining.

Revisiting Pretraining with Adapters

Typically, methods for improving weakly supervised learning are tailored towards domain-specific tasks or integrated with a specific model architecture. That makes them hard to be compared or transferred across tasks without extensive adjustments. We introduce Knodle: a framework that provides accessibility, data abstraction, and model independence. It allows easy experimentations and benchmarking of different denoising algorithms, tasks, and datasets. 
Knodle is published as an open-source Python package knodle and also available at https://github.com/knodle/knodle.

Knodle: Modular Weakly Supervised Learning with PyTorch

Task-oriented compositional semantic parsing (TCSP) handles complex nested user queries and serves as an essential component of virtual assistants.
Current TCSP models rely on numerous training data to achieve decent performance but fail to generalize to low-resource target languages or domains.
In this paper, we present X2Parser, a transferable Cross-lingual and Cross-domain Parser for TCSP. Unlike previous models that learn to generate the hierarchical representations for nested intents and slots, we propose to predict flattened intents and slots representations separately and cast both prediction tasks into sequence labeling problems. After that, we further propose a fertility-based slot predictor that first learns to dynamically detect the number of labels for each token, and then predicts the slot types. Experimental results illustrate that our model can significantly outperform existing strong baselines in cross-lingual and cross-domain settings, and our model can also achieve a good generalization ability on target languages of target domains. Furthermore, our model tackles the problem in an efficient non-autoregressive way that reduces the latency by up to 66\% compared to the generative model.

X2Parser: Cross-Lingual and Cross-Domain Framework for Task-Oriented Compositional Semantic Parsing

To highlight the challenges of achieving representation disentanglement for text domain in an unsupervised setting, in this paper we select a representative set of successfully applied models from the image domain. We evaluate these models on 6 disentanglement metrics, as well as on downstream classification tasks and homotopy. To facilitate the evaluation, we propose two synthetic datasets with known generative factors. Our experiments highlight the existing gap in the text domain and illustrate that certain elements such as representation sparsity (as an inductive bias), or representation coupling with the decoder could impact disentanglement. To the best of our knowledge, our work is the first attempt on the intersection of unsupervised representation disentanglement and text, and provides the experimental framework and datasets for examining future developments in this direction.

Learn The Big Picture: Representation Learning for Clustering

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