fix README.md (facebookresearch#2735)

Summary:
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## What does this PR do?
Fixes # (issue).

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Pull Request resolved: facebookresearch#2735

Reviewed By: myleott

Differential Revision: D24343492

Pulled By: xianxl

fbshipit-source-id: c61c717756307036f9d89de5a8ded66784f1acf7

examples/latent_depth/README.md

@@ -1,19 +1,19 @@
 # Deep Transformers with Latent Depth (Li et al., 2020)
 
-[https://arxiv.org/abs/2009.13102] (https://arxiv.org/abs/2009.13102).
+[https://arxiv.org/abs/2009.13102](https://arxiv.org/abs/2009.13102).
 
 ## Introduction
 
 We present a probabilistic framework to automatically learn which layer(s) to use by learning the posterior distributions of layer selection. As an extension of this framework, we propose a novel method to train one shared Transformer network for multilingual machine translation with different layer selection posteriors for each language pair.
 
 ## Training a multilingual model with latent depth
 
-Below is an example of training with latent depth in decoder for one-to-many (O2M) related languages. We use the same preprocessed (numberized and binarized) TED8 dataset as in [Balancing Training for Multilingual Neural Machine Translation (Wang et al., 2020)] (https://github.com/cindyxinyiwang/multiDDS), which could be generated by [the script] (https://github.com/cindyxinyiwang/multiDDS/blob/multiDDS/util_scripts/prepare_multilingual_data.sh) the author provided.
+Below is an example of training with latent depth in decoder for one-to-many (O2M) related languages. We use the same preprocessed (numberized and binarized) TED8 dataset as in [Balancing Training for Multilingual Neural Machine Translation (Wang et al., 2020)](https://github.com/cindyxinyiwang/multiDDS), which could be generated by [the script](https://github.com/cindyxinyiwang/multiDDS/blob/multiDDS/util_scripts/prepare_multilingual_data.sh) the author provided.
 ```bash
 lang_pairs_str="eng-aze,eng-bel,eng-ces,eng-glg,eng-por,eng-rus,eng-slk,eng-tur"
 databin_dir=<path to binarized data>
 
-python fairseq_cli/train.py ${databin_dir} \
+fairseq-train ${databin_dir} \
   --user-dir, examples/latent_depth/src \
   --lang-pairs "${lang_pairs_str}" \
   --arch multilingual_transformer_iwslt_de_en \
@@ -50,7 +50,7 @@ src_lang=<source language to translate from>
 tgt_lang=<target language to translate to>
 gen_data=<name of data split, e.g. valid, test, etc>
 
-python fairseq_cli/generate.py ${databin_dir} \
+fairseq-generate ${databin_dir} \
   --path ${model_path} \
   --task multilingual_translation_latent_depth \
   --decoder-latent-layer \