Thailand

Conventional autoregressive models have achieved great success in text generation but suffer from the exposure bias problem in that token sequences in the training and in the generation stages are mismatched.
While generative adversarial networks (GANs) can remedy this problem, existing implementations of GANs directly on the discrete outputs tend to be unstable and lack diversity.
In this work, we propose TILGAN, a Transformer-based Implicit Latent GAN, which combines a Transformer autoencoder and GAN in the latent space with a novel design and distribution matching based on the Kullback-Leibler (KL) divergence.
Specifically, to improve the local and global coherence, we explicitly introduce a multi-scale discriminator to capture the semantic information at varying scales among the sequence of hidden representations encoded by Transformer. 
Moreover, the decoder is enhanced by an additional KL loss to be consistent with the latent-generator. 
Experimental results on three benchmark datasets demonstrate the validity and effectiveness of our model, by obtaining significant improvements and a better quality-diversity trade-off in automatic and human evaluation for both unconditional and conditional generation tasks. Our code is available at https://github.com/shizhediao/TILGAN.

IJCNLP-AACL 2021

TILGAN: Transformer-based Implicit Latent GAN for Diverse and Coherent Text Generation

generative adversarial networks

text generation

transformer

**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).

**Reminder:** It is ACL’s policy that at least one author of each accepted paper (including ACL Finding papers) must register for the conference.

**Reminder2:** Underline site will open closer to the event. If you already registered you will receive access detail

Registration is now open

The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP).

technical paper

Parallel cross-lingual summarization data is scarce, requiring models to better use the limited available cross-lingual resources. Existing methods to do so often adopt sequence-to-sequence networks with multi-task frameworks. Such approaches apply multiple decoders, each of which is utilized for a specific task. However, these independent decoders share no parameters, hence fail to capture the relationships between the discrete phrases of summaries in different languages, breaking the connections in order to transfer the knowledge of the high-resource languages to low-resource languages. To bridge these connections, we propose a novel Multi-Task framework for Cross-Lingual Abstractive Summarization (MCLAS) in a low-resource setting. Employing one unified decoder to generate the sequential concatenation of monolingual and cross-lingual summaries, MCLAS makes the monolingual summarization task a prerequisite of the CLS task. In this way, the shared decoder learns interactions involving alignments and summary patterns across languages, which encourages attaining knowledge transfer. Experiments on two CLS datasets demonstrate that our model significantly outperforms three baseline models in both low-resource and full-dataset scenarios. Moreover, in-depth analysis on the generated summaries and attention heads verifies that interactions are learned well using MCLAS, which benefits the CLS task under limited parallel resources.

Cross-Lingual Abstractive Summarization with Limited Parallel Resources

Despite the prominence of neural abstractive summarization models, we know little about how they actually form summaries and how to understand where their decisions come from.
We propose a two-step method to interpret summarization model decisions. 
We first analyze the model's behavior by ablating the full model to categorize each decoder decision into one of several generation modes: roughly, is the model behaving like a language model, is it relying heavily on the input, or is it somewhere in between?
After isolating decisions that do depend on the input, we explore interpreting these decisions using several different attribution methods.
We compare these techniques based on their ability to select content and reconstruct the model's predicted token from perturbations of the input, thus revealing whether highlighted attributions are truly important for the generation of the next token.
While this machinery can be broadly useful even beyond summarization, we specifically demonstrate its capability to identify phrases the summarization model has memorized and determine where in the training pipeline this memorization happened, as well as study complex generation phenomena like sentence fusion on a per-instance basis.

Dissecting Generation Modes for Abstractive Summarization Models via Ablation and Attribution

In this paper, we present a conceptually simple while empirically powerful framework for abstractive summarization, SimCLS, which can bridge the gap between the learning objective and evaluation metrics resulting from the currently dominated sequence-to-sequence learning framework by formulating text generation as a reference-free evaluation problem (i.e., quality estimation) assisted by contrastive learning.
Experimental results show that, with minor modification over existing top-scoring systems, SimCLS can improve the performance of existing top-performing models by a large margin.
Particularly, 2.51 absolute improvement against BART and 2.50 over PEGASUS w.r.t ROUGE-1 on the CNN/DailyMail dataset, driving the state-of-the-art performance to a new level.
We have open-sourced our codes and results: https://github.com/yixinL7/SimCLS.
Results of our proposed models have been deployed into ExplainaBoard platform, which allows researchers to understand our systems in a more fine-grained way.

SimCLS: A Simple Framework for Contrastive Learning of Abstractive Summarization

Static word embeddings that represent words by a single vector 
cannot capture the variability of word meaning in different linguistic
and extralinguistic contexts. Building on prior work on contextualized 
and dynamic word embeddings, we introduce dynamic contextualized 
word embeddings that represent words as a function of both linguistic
and extralinguistic context. Based on a pretrained language model (PLM), 
dynamic contextualized word embeddings model time and social space 
jointly, which makes them attractive for a range of NLP tasks involving
semantic variability. We highlight potential application scenarios by means 
of qualitative and quantitative analyses on four English datasets.

Dynamic Contextualized Word Embeddings

While there is a large amount of research in the field of Lexical Semantic Change Detection, only few approaches go beyond a standard benchmark evaluation of existing models. In this paper, we propose a shift of focus from change detection to change discovery, i.e., discovering novel word senses over time from the full corpus vocabulary. By heavily fine-tuning a type-based and a token-based approach on recently published German data, we demonstrate that both models can successfully be applied to discover new words undergoing meaning change. Furthermore, we provide an almost fully automated framework for both evaluation and discovery.

Lexical Semantic Change Discovery

Supervised learning methods have proven to be effective for Aspect-Based Sentiment Analysis (ABSA).
However, the lack of fine-grained labeled data hinders their effectiveness in many domains. To address this issue, unsupervised domain adaptation methods are desired to transfer knowledge from a labeled source domain to any unlabeled target domain. In this paper, we propose a new domain adaptation paradigm called cross-domain review generation (CDRG), which aims to generate target-domain reviews with fine-grained annotation based on the source-domain labeled reviews. To achieve this goal, we propose a two-step approach as a concrete realization of CDRG. It first converts a source-domain review to a domain-independent review by masking its source-specific attributes, and then converts the domain-independent review to a target-domain review with a masked language model pre-trained in the target domain. We further propose two ways to leverage the generated target-domain reviews for two cross-domain ABSA tasks. Extensive experiments demonstrate the superiority of our CDRG-based approaches over the state-of-the-art domain adaptation methods.

Cross-Domain Review Generation for Aspect-Based Sentiment Analysis

Named entity recognition (NER) is a fundamental task in natural language processing, these is a long held belief that datasets benefit the model. However, not all the data help with generalization, and some samples may contain ambiguous entities or noisy labels. The existing methods can not distinguish hard samples from noisy samples well, and becomes particularly challenging in the case of overfitting. This paper proposes a new method called Noise-Aware-with-Filter (NAF) to solve the issues from two sides. From the perspective of the data, we design a Logit-Maximum-Difference (LMD) mechanism, which maximizes the diversity between different samples to help the model identify noisy samples. From the perspective of the model, we design an Incomplete-Trust (In-trust) loss function, which boosts $L_{CRF}$ with a robust Distrust-Cross-Entropy(DCE) term. Our proposed In-trust can effectively alleviate the overfitting caused by previous loss function. Experiments on six real-world Chinese and English NER datasets show that NAF outperforms the previous methods, and which obtained the state-of-the-art(SOTA) results on the CoNLL2003 and CoNLL++ datasets.

Named Entity Recognition via Noise Aware Training Mechanism with Data Filter

Pruning has been demonstrated as an effective way of reducing computational complexity for deep networks, especially CNNs for computer vision tasks. In this paper, we investigate the opportunity to accelerate the inference of large-scale pre-trained language model via pruning. We propose EBERT, a dynamic structured pruning algorithm for efficient BERT inference. Unlike previous methods that randomly prune the model weights for static inference, EBERT dynamically determines and prunes the unimportant heads in multi-head self-attention layers and the unimportant structured computations in feed-forward network for each input sample at run-time. Experimental results show that our proposed EBERT outperforms other state-of-the-art methods on different tasks.

EBERT: Efficient BERT Inference with Dynamic Structured Pruning

How to combine several pieces of evidence to verify a claim is an interesting semantic task. Very complex methods have been proposed, combining different evidence vectors using an evidence interaction graph. In this paper, we show that in case of inference based on transformer models, two effective approaches use either (i) a simple application of max pooling over the Transformer evidence vectors; or (ii) computing a weighted sum of the evidence vectors. Our experiments on the FEVER claim verification task show that the methods above achieve the state of the art, constituting strong baseline for much more computationally complex methods.

Strong and Light Baseline Models for Fact-Checking Joint Inference

Adapting a model to a handful of personalized data is challenging, especially when it has gigantic parameters, such as a Transformer-based pretrained model. The standard way of fine-tuning all the parameters necessitates storing a huge model for each user. In this work, we introduce a lightweight approach dubbed UserAdapter, which clamps hundred millions of parameters of the Transformer model and optimizes a tiny user-specific vector. We take sentiment analysis as a test bed, and collect datasets of reviews from Yelp and IMDB respectively. Results show that, on both datasets, UserAdapter achieves better accuracy than the standard fine-tuned Transformer-based pre-trained model. More importantly, UserAdapter offers an efficient way to produce a personalized Transformer model with less than 0.5\% parameters added for each user.

TILGAN: Transformer-based Implicit Latent GAN for Diverse and Coherent Text Generation

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